
  ___  ____  ____  ____  ____ ®
 /__    /   ____/   /   ____/      Stata 18.0
___/   /   /___/   /   /___/       MP—Parallel Edition

 Statistics and Data Science       Copyright 1985-2023 StataCorp LLC
                                   StataCorp
                                   4905 Lakeway Drive
                                   College Station, Texas 77845 USA
                                   800-782-8272        https://www.stata.com
                                   979-696-4600        service@stata.com

Stata license: 745-user 2-core network, expiring 30 Jun 2025
Serial number: 501809309429
  Licensed to: Noah Sobel-Lewin
               University of Chicago

Notes:
      1. Stata is running in batch mode.
      2. Unicode is supported; see help unicode_advice.
      3. More than 2 billion observations are allowed; see help obs_advice.
      4. Maximum number of variables is set to 5,000 but can be increased;
          see help set_maxvar.

. do 01Code/phone_survey/00MainStata.do 

. /************************************************************************
> Purpose:        Master do-file for ETS Phone Survey Analysis                 
>    
> *************************************************************************/
. 
. global CODE_DIR = "01Code/phone_survey"

. 
. global PHONE_DATA_IN = "00RawData/phone_survey"

. global PHONE_DATA_OUT = "02DataPipeline/phone_survey"

. global PHONE_TABS = "03Output/tables/"

. 
. global BASELINE_DATA_IN = "00RawData/baseline"

. global BASELINE_DATA_OUT = "02DataPipeline/baseline"

. global EMISSIONS_DATA_OUT = "02DataPipeline/emissions"

. global TRADING_DATA_IN = "00RawData/trading"

. global TRADING_DATA_OUT = "02DataPipeline/trading/intermediate"

. global TRADING_DATA_CLEAN = "02DataPipeline/trading/cleaned"

. 
. ** We use a conversion rate of USD 1 to INR 70, as of 2 Jan 2019, 
. ** Source: The Fed (https://www.federalreserve.gov/releases/h10/20190107/)
. global USD2INR=70

. grstyle init

. 
. **********************************************************************
. 
. ** Clean phone survey
. do "$CODE_DIR/edit_phone_survey_variables.do"

. /************************************************************************
> Purpose:        Adjust Phone Survey variables to fit our analysis            
>            
> *************************************************************************/
. 
. use "$PHONE_DATA_IN/Phone Survey All Covariates (Plant).dta", clear
(Phone Survey 2020: Baseline covariates (Cleaned, Plant Level))

. 
. * Set missing maintenance to 0
. replace pv_modification_cost = 0 if pv_modification_cost == . 
(78 real changes made)

. replace bh_annu_maint_cost_lakh = 0 if bh_annu_maint_cost_lakh == . 
(267 real changes made)

. replace bh_cleaning_cost = 0 if bh_cleaning_cost == . 
(89 real changes made)

. 
. * Remove component which makes maintenance cost non additive; add present val
> ue of modification
. replace bh_annu_maint_cost_lakh = bh_annu_maint_cost_lakh - (bh_cleaning_cost
> /100000) + pv_modification_cost
(283 real changes made)

. replace bh_annu_maint_cost_lakh = round(bh_annu_maint_cost_lakh, 0.001)
(111 real changes made)

. 
. *****************************************************************************
> ***
. * Boiler House Fuel Cost 
. * this can be added back to any of the other iterations of total boiler cost
. *****************************************************************************
> ***
. 
. foreach i in 18_19 19_20{
  2. 
.         ds fuel_`i'_cost_*
  3.         foreach var in `r(varlist)'  {
  4.                 replace `var' = 0 if `var' == .
  5.                 gen temp_`var' = .
  6.         }
  7. 
.         replace fuel_`i'_cost_1 = 0 if imported_coal == 0 & imp_coal_lignite 
>  == 0 & imp_coal_indian  == 0 
  8.         replace fuel_`i'_cost_2 = 0 if diesel == 0
  9.         replace fuel_`i'_cost_3 = 0 if lignite == 0 & imp_coal_lignite == 
> 0
 10.         replace fuel_`i'_cost_4 = 0 //LDO is not used as boiler fuel
 11.         replace fuel_`i'_cost_5 = 0 if imported_coal == 0 & imp_coal_ligni
> te  == 0 & imp_coal_indian  == 0 & lignite == 0  
 12.         replace fuel_`i'_cost_6 = 0 //Natural Gas is not used as boiler fu
> el
 13.         replace fuel_`i'_cost_7 = 0 if other_solid_fuel == 0 & bagasse == 
> 0
 14.         replace fuel_`i'_cost_8 = 0 if wood == 0
 15.         replace fuel_`i'_cost_9 = 0 //bio diesel not boiler fuel
 16. 
.         gen boi_fuel_`i'_lakh = 0
 17.         ds fuel_`i'_cost_*
 18.         foreach var in `r(varlist)'  {
 19.                 replace boi_fuel_`i'_lakh = boi_fuel_`i'_lakh + `var'
 20.         }
 21. }
fuel_18~st_1  fuel_18~st_3  fuel_18~st_5  fuel_18~st_7  fuel_18~st_9
fuel_18~st_2  fuel_18~st_4  fuel_18~st_6  fuel_18~st_8
(136 real changes made)
(373 missing values generated)
(193 real changes made)
(373 missing values generated)
(233 real changes made)
(373 missing values generated)
(363 real changes made)
(373 missing values generated)
(360 real changes made)
(373 missing values generated)
(369 real changes made)
(373 missing values generated)
(373 real changes made)
(373 missing values generated)
(372 real changes made)
(373 missing values generated)
(372 real changes made)
(373 missing values generated)
(0 real changes made)
(162 real changes made)
(6 real changes made)
(9 real changes made)
(0 real changes made)
(4 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
fuel_18~st_1  fuel_18~st_3  fuel_18~st_5  fuel_18~st_7  fuel_18~st_9
fuel_18~st_2  fuel_18~st_4  fuel_18~st_6  fuel_18~st_8
(235 real changes made)
(1 real change made)
(131 real changes made)
(0 real changes made)
(13 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
(0 real changes made)
fuel_19~st_1  fuel_19~st_3  fuel_19~st_5  fuel_19~st_7  fuel_19~st_9
fuel_19~st_2  fuel_19~st_4  fuel_19~st_6  fuel_19~st_8
(131 real changes made)
(373 missing values generated)
(195 real changes made)
(373 missing values generated)
(225 real changes made)
(373 missing values generated)
(362 real changes made)
(373 missing values generated)
(361 real changes made)
(373 missing values generated)
(369 real changes made)
(373 missing values generated)
(373 real changes made)
(373 missing values generated)
(372 real changes made)
(373 missing values generated)
(372 real changes made)
(373 missing values generated)
(0 real changes made)
(172 real changes made)
(6 real changes made)
(11 real changes made)
(0 real changes made)
(4 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
fuel_19~st_1  fuel_19~st_3  fuel_19~st_5  fuel_19~st_7  fuel_19~st_9
fuel_19~st_2  fuel_19~st_4  fuel_19~st_6  fuel_19~st_8
(242 real changes made)
(1 real change made)
(140 real changes made)
(0 real changes made)
(12 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made)
(0 real changes made)

. 
. replace boi_fuel_18_19_lakh = . if fuel_cost_total_1819==.
(136 real changes made, 136 to missing)

. replace boi_fuel_19_20_lakh = . if fuel_cost_total_1920==.
(126 real changes made, 126 to missing)

. 
. label var boi_fuel_18_19_lakh "Boiler Fuel Cost 18-19 (INR Lakhs)"

. label var boi_fuel_19_20_lakh "Boiler Fuel Cost 19-20 (INR Lakhs)"

. 
. *****************************************************************************
> ***
. * Labor Costs 
. *****************************************************************************
> ***
. 
. * Office and prod costs should be nonzero; otherwise set as missing
. replace tot_office_worker_cost = . if tot_office_worker_cost==0
(23 real changes made, 23 to missing)

. replace tot_prod_worker_cost = . if tot_prod_worker_cost==0
(25 real changes made, 25 to missing)

. 
. // reorganize BH costs
. 
. * Fix an outlier
. replace c6_3_boi_helper_sal = 12000 if c6_3_boi_helper_sal == 120000
(1 real change made)

. 
. * Create temporary dummy if no labor costs
. gen d_bh_labor_costs = 0

. ds c6_3_*
c6_3_boi_e~t  c6_3_boi_m~t  c6_3_boi_t~t  c6_3_boi_o~t  c6_3_boi_h~t
c6_3_boi_e~l  c6_3_boi_m~l  c6_3_boi_t~l  c6_3_boi_o~l  c6_3_boi_h~l

. foreach var of varlist `r(varlist)' {
  2.         replace d_bh_labor_costs = 1 if !missing(`var')
  3. }
(273 real changes made)
(1 real change made)
(5 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

. 
. * Set missing values of BH engineer/masters costs to 0 (plausible they don't 
> have any), 
. * as long as they have some BH labor costs
. foreach var of varlist bh_engineer_cost bh_an_master_cost {
  2.         replace `var' = 0 if `var' == . & d_bh_labor_costs==1
  3.         replace `var' = . if d_bh_labor_costs==0
  4. }
(1 real change made)
(1 real change made, 1 to missing)
(12 real changes made)
(1 real change made, 1 to missing)

. 
. // replace bh_annu_labor_cost with clean c6_3_boi_[tech/oper/helper] costs
. ** these look like they have better overall estimates for the plant
. 
. * if there is staff count, but no cost, then impute median cost of that categ
> ory
. foreach var in techni opera helper {
  2.         quietly sum c6_3_boi_`var'_sal if d_bh_labor_costs==1, d
  3.         replace c6_3_boi_`var'_sal = `r(p50)' if inlist(c6_3_boi_`var'_sal
> , ., 0) & !inlist(c6_3_boi_`var'_tot, ., 0)
  4. }
(9 real changes made)
(10 real changes made)
(10 real changes made)

. 
. * Set values to 0 or missing, depending on if any costs reported or not
. ds c6_3_boi_techni_* c6_3_boi_opera_* c6_3_boi_helper_*
c6_3_boi_t~t  c6_3_boi_o~t  c6_3_boi_h~t
c6_3_boi_t~l  c6_3_boi_o~l  c6_3_boi_h~l

. foreach var of varlist `r(varlist)' {
  2.         replace `var' = . if d_bh_labor_costs==0
  3.         replace `var' = 0 if missing(`var') & d_bh_labor_costs==1
  4. }
(0 real changes made)
(5 real changes made)
(0 real changes made)
(4 real changes made)
(0 real changes made)
(32 real changes made)
(0 real changes made)
(32 real changes made)
(0 real changes made)
(12 real changes made)
(0 real changes made)
(12 real changes made)

. 
. * Aggregate (recall, these variables are monthly)
. replace bh_annu_labor_cost = 0 
(373 real changes made)

. foreach var in techni opera helper {
  2.         replace bh_annu_labor_cost = bh_annu_labor_cost + 12 * c6_3_boi_`v
> ar'_tot * c6_3_boi_`var'_sal
  3. }
(368 real changes made, 94 to missing)
(247 real changes made)
(267 real changes made)

. replace bh_annu_labor_cost = bh_annu_labor_cost/100000
(276 real changes made)

. replace bh_annu_labor_cost = . if d_bh_labor_costs==0
(0 real changes made)

. *drop d_bh_labor_costs
. 
. // replace bh_annu_labor_cost with the c6_3_ versions from the accounts
. 
. 
. save "$PHONE_DATA_OUT/Phone Survey All Covariates (Plant)_Analysis.dta", repl
> ace
(file 02DataPipeline/phone_survey/Phone Survey All Covariates
    (Plant)_Analysis.dta not found)
file 02DataPipeline/phone_survey/Phone Survey All Covariates
    (Plant)_Analysis.dta saved

. 
end of do-file

. 
. ** Merge baseline survey
. do "$CODE_DIR/prepare_merged_dataset.do"

. /*********************************************************************
> Purpose:        Merge and prepare phone survey and baseline covariates for an
> alysis.
> *********************************************************************/
. 
.         set more off

.         clear matrix

.         clear all

.         *ssc install estout
.         
. *------------------------------------------------------------------------
.         use "$PHONE_DATA_OUT/Phone Survey All Covariates (Plant)_Analysis.dta
> ", clear
(Phone Survey 2020: Baseline covariates (Cleaned, Plant Level))

. 
.         
. *****************************************************************************
. ****** Create Factors of Production from Phone Survey (Gargi's Table 5) *****
. *****************************************************************************
. * note: this code is all copied from Gargi's code to produce her Aggregated F
> actors of Production balance check table
. 
.         keep if surveyed == 1 
(78 observations deleted)

.         label var bh_annu_water_cost "BH Annual Water Cost (INR 000's)"

.         replace bh_annu_water_cost = bh_annu_water_cost/1000
(137 real changes made)

.         label var ph_tot_water_cost  "PH Annual Water Cost (INR 000's)"

.         replace ph_tot_water_cost = ph_tot_water_cost/1000*annu_work_days
(141 real changes made)

.         label var ph_tot_rawmtrl_cost "Annual Fabric Cost (INR Lakhs)"

.         label var ph_tot_chem_cost "Annual PH Chemical Cost (INR 000's)"

.         replace ph_tot_chem_cost = ph_tot_chem_cost*100000/1000*annu_work_day
> s
(270 real changes made)

.         label var tot_chem_cost_etp "Annual ETP Chemical Cost (INR 000's)"

.         replace tot_chem_cost_etp = tot_chem_cost_etp /1000*annu_work_days
(180 real changes made)

.         label var bh_annu_chem_cost  "Annual BH Chemical Cost (INR 000's)"

.         replace bh_annu_chem_cost = bh_annu_chem_cost /1000
(269 real changes made)

.         label var bh_annu_ope_cost_lakh "Annual BH Operating Cost (INR Lakhs)
> "

.         label var bh_annu_maint_cost_lakh "Annual BH Maitnenance Cost (INR La
> khs)"

.         label var bh_alt_annu_labor_cost "Alt: BH Annual Labour Cost (INR Lak
> hs)"

.         label var bh_annu_labor_cost  "BH Annual Worker Cost (INR Lakhs)"

.         label var bh_annu_elec_cost "BH Electricity Cost"

.         label var bh_annu_inputs_cost "BH Inputs Cost"

.         label var treat "ETS Treatment=1"

.         
. //      Generate key aggregate non-BH variables // plant-level variables     
>    
.         
.         * Total Revenue
.                 rename tot_rev_19_1 plant_rev

.         
.         * Capital = bh annualized installation, modification; bh repair, oper
> ating, maint
.                 
.                 * fixed capital
.                 gen pv_fixed_capital = pv_instllation_cost + pv_modification_
> cost
(68 missing values generated)

.                 label var pv_fixed_capital  "Total Fixed Capital (INR Lakhs)"

. 
.                 * working capital
.                 * set missing values to 0
.                 foreach var of varlist bh_total_smallrepair bh_annu_ope_cost_
> lakh bh_annu_maint_cost_lakh {
  2.                         replace `var' = 0 if `var' == .
  3.                 }
(184 real changes made)
(26 real changes made)
(0 real changes made)

.                 
.                 * create aggregate variable
.                 gen working_capital = bh_total_smallrepair + bh_annu_ope_cost
> _lakh + bh_annu_maint_cost_lakh 

.                 label var working_capital  "Working Capital (INR Lakhs)"

.                 
.                 * replace 0 values with missing again
.                 foreach var of varlist bh_total_smallrepair bh_annu_ope_cost_
> lakh bh_annu_maint_cost_lakh {
  2.                         replace `var' = . if `var' == 0
  3.                 }
(261 real changes made, 261 to missing)
(26 real changes made, 26 to missing)
(12 real changes made, 12 to missing)

.         
.                 * total capital (= leave missing as missing; most are fixed c
> apital)
.                 gen plant_cost_capital = working_capital + pv_fixed_capital
(68 missing values generated)

.                 label var plant_cost_capital  "Total Capital (INR Lakhs)"

. 
.         * Labor Costs = office, prod + bh daily, engineer, master 
.                 
.                 * create aggregate variable             // can do the c6_ etc
> . computations if we want 
.                 generate plant_cost_labor = tot_office_worker_cost * 12 + tot
> _prod_worker_cost * 12 + bh_annu_labor_cost + bh_engineer_cost + bh_an_master
> _cost
(29 missing values generated)

.                 label var plant_cost_labor  "Total Labor Cost (INR Lakhs)"

.         
.         * Fuel Costs = fuel
.                 gen plant_cost_fuel = total_fuel_cost_1920
(48 missing values generated)

.                 label var plant_cost_fuel  "Total Fuel Cost (INR Lakhs)"

.                 
.                 * also create the bl from previous year
.                 gen plant_cost_fuel_bl = total_fuel_cost_1819
(58 missing values generated)

.                 label var plant_cost_fuel_bl  "Total Fuel Cost (INR Lakhs) - 
> Baseline"

.         
.         * Electricity Cost = elec
.                 gen plant_cost_elec = elecbill_2019
(17 missing values generated)

.                 label var plant_cost_elec  "Total Electricity Cost (INR Lakhs
> )"

. 
.         * Material Costs = raw material, water, chem
.         
.                 * set missing values to 0
.                 foreach var of varlist ph_tot_rawmtrl_cost ph_tot_water_cost 
> bh_annu_water_cost ///
>                 ph_tot_chem_cost tot_chem_cost_etp bh_annu_chem_cost {
  2.                         tab `var'
  3.                         replace `var' = 0 if `var' == .
  4.                 }

     Annual |
Fabric Cost |
(INR Lakhs) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        263       95.64       95.64
        495 |          1        0.36       96.00
    595.608 |          1        0.36       96.36
        891 |          1        0.36       96.73
       1515 |          1        0.36       97.09
       3000 |          1        0.36       97.45
     3643.2 |          1        0.36       97.82
    4245.92 |          1        0.36       98.18
      10880 |          1        0.36       98.55
      12402 |          1        0.36       98.91
    15240.9 |          1        0.36       99.27
   77059.02 |          1        0.36       99.64
   130070.3 |          1        0.36      100.00
------------+-----------------------------------
      Total |        275      100.00
(20 real changes made)

  PH Annual |
 Water Cost |
(INR 000's) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        146       50.87       50.87
      19.32 |          1        0.35       51.22
      410.4 |          1        0.35       51.57
      801.9 |          1        0.35       51.92
        915 |          1        0.35       52.26
      943.2 |          1        0.35       52.61
       1120 |          1        0.35       52.96
     1293.6 |          1        0.35       53.31
     1300.9 |          1        0.35       53.66
    1304.64 |          1        0.35       54.01
   1319.932 |          1        0.35       54.36
   1513.688 |          1        0.35       54.70
   1622.221 |          1        0.35       55.05
     1663.2 |          1        0.35       55.40
       1824 |          1        0.35       55.75
   1992.775 |          1        0.35       56.10
       2200 |          1        0.35       56.45
     2317.5 |          1        0.35       56.79
   2344.125 |          1        0.35       57.14
     2365.2 |          1        0.35       57.49
       2600 |          1        0.35       57.84
    3110.24 |          1        0.35       58.19
     3110.4 |          1        0.35       58.54
   3253.626 |          1        0.35       58.89
   3288.276 |          1        0.35       59.23
    3470.04 |          1        0.35       59.58
   3508.056 |          1        0.35       59.93
    3535.84 |          1        0.35       60.28
     3549.6 |          1        0.35       60.63
   3550.725 |          1        0.35       60.98
       3604 |          1        0.35       61.32
   3702.132 |          1        0.35       61.67
       3750 |          1        0.35       62.02
     3766.5 |          1        0.35       62.37
       3920 |          1        0.35       62.72
       3975 |          1        0.35       63.07
     4009.5 |          1        0.35       63.41
    4018.56 |          1        0.35       63.76
     4032.6 |          1        0.35       64.11
     4060.8 |          1        0.35       64.46
     4114.8 |          1        0.35       64.81
       4140 |          1        0.35       65.16
    4146.78 |          1        0.35       65.51
   4168.536 |          1        0.35       65.85
   4176.984 |          1        0.35       66.20
   4211.352 |          1        0.35       66.55
       4224 |          1        0.35       66.90
   4406.458 |          1        0.35       67.25
     4427.5 |          1        0.35       67.60
   4673.025 |          1        0.35       67.94
   4704.248 |          1        0.35       68.29
       4732 |          1        0.35       68.64
    4777.92 |          1        0.35       68.99
   4897.442 |          1        0.35       69.34
     4902.1 |          1        0.35       69.69
     4972.5 |          1        0.35       70.03
   5001.822 |          1        0.35       70.38
       5025 |          1        0.35       70.73
   5164.004 |          1        0.35       71.08
       5280 |          1        0.35       71.43
   5443.792 |          1        0.35       71.78
       5460 |          1        0.35       72.13
     5533.5 |          1        0.35       72.47
       5577 |          1        0.35       72.82
       5600 |          1        0.35       73.17
       5621 |          1        0.35       73.52
   5681.787 |          1        0.35       73.87
   5756.638 |          1        0.35       74.22
       5980 |          1        0.35       74.56
     6019.2 |          1        0.35       74.91
   6103.733 |          1        0.35       75.26
    6243.12 |          1        0.35       75.61
   6248.907 |          1        0.35       75.96
     6330.8 |          1        0.35       76.31
       6372 |          1        0.35       76.66
     6505.2 |          1        0.35       77.00
       6552 |          1        0.35       77.35
       6555 |          1        0.35       77.70
       6615 |          1        0.35       78.05
    6650.04 |          1        0.35       78.40
     6655.2 |          1        0.35       78.75
   6980.815 |          1        0.35       79.09
     7028.1 |          1        0.35       79.44
       7260 |          1        0.35       79.79
   7264.411 |          1        0.35       80.14
   7278.214 |          1        0.35       80.49
       7325 |          1        0.35       80.84
       7371 |          1        0.35       81.18
       7434 |          1        0.35       81.53
   7452.172 |          1        0.35       81.88
   7482.956 |          1        0.35       82.23
   7712.536 |          1        0.35       82.58
    7732.69 |          1        0.35       82.93
       7748 |          1        0.35       83.28
   7819.535 |          1        0.35       83.62
       7938 |          1        0.35       83.97
     8044.4 |          1        0.35       84.32
   8106.315 |          1        0.35       84.67
       8127 |          1        0.35       85.02
     8224.8 |          1        0.35       85.37
     8257.5 |          1        0.35       85.71
     8337.5 |          1        0.35       86.06
   8963.276 |          1        0.35       86.41
   8993.856 |          1        0.35       86.76
     9266.4 |          1        0.35       87.11
    9765.43 |          1        0.35       87.46
   9891.498 |          1        0.35       87.80
   9999.504 |          1        0.35       88.15
   10010.72 |          1        0.35       88.50
   10057.12 |          1        0.35       88.85
   10058.85 |          1        0.35       89.20
   10140.21 |          1        0.35       89.55
      10296 |          1        0.35       89.90
   10450.18 |          1        0.35       90.24
   10668.37 |          1        0.35       90.59
   10814.68 |          1        0.35       90.94
      11136 |          1        0.35       91.29
   11509.89 |          1        0.35       91.64
   11530.22 |          1        0.35       91.99
      11700 |          1        0.35       92.33
    11947.5 |          1        0.35       92.68
      11970 |          1        0.35       93.03
   12026.77 |          1        0.35       93.38
   12231.94 |          1        0.35       93.73
    12408.5 |          1        0.35       94.08
    12650.4 |          1        0.35       94.43
    12877.5 |          1        0.35       94.77
   13460.72 |          1        0.35       95.12
   14849.99 |          1        0.35       95.47
   14937.39 |          1        0.35       95.82
   14994.51 |          1        0.35       96.17
    15015.2 |          1        0.35       96.52
    15284.5 |          1        0.35       96.86
   16058.24 |          1        0.35       97.21
    16625.1 |          1        0.35       97.56
      18011 |          1        0.35       97.91
   19795.82 |          1        0.35       98.26
      24485 |          1        0.35       98.61
   26257.26 |          1        0.35       98.95
   35244.45 |          1        0.35       99.30
   36013.62 |          1        0.35       99.65
   36286.76 |          1        0.35      100.00
------------+-----------------------------------
      Total |        287      100.00
(8 real changes made)

  BH Annual |
 Water Cost |
(INR 000's) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        143       51.07       51.07
       42.4 |          1        0.36       51.43
      65.78 |          1        0.36       51.79
      74.25 |          1        0.36       52.14
         80 |          1        0.36       52.50
       89.7 |          1        0.36       52.86
      115.5 |          1        0.36       53.21
    137.126 |          1        0.36       53.57
    143.325 |          1        0.36       53.93
     153.44 |          1        0.36       54.29
     187.53 |          1        0.36       54.64
    221.184 |          1        0.36       55.00
     236.88 |          1        0.36       55.36
     250.75 |          1        0.36       55.71
    256.575 |          1        0.36       56.07
    263.592 |          1        0.36       56.43
     288.12 |          1        0.36       56.79
        309 |          1        0.36       57.14
     316.25 |          1        0.36       57.50
        336 |          1        0.36       57.86
     336.96 |          1        0.36       58.21
     348.48 |          1        0.36       58.57
      356.4 |          1        0.36       58.93
      358.8 |          1        0.36       59.29
     360.36 |          1        0.36       59.64
      361.4 |          1        0.36       60.00
     361.62 |          1        0.36       60.36
     369.36 |          1        0.36       60.71
      370.3 |          1        0.36       61.07
        392 |          1        0.36       61.43
      397.5 |          1        0.36       61.79
        402 |          1        0.36       62.14
     403.26 |          1        0.36       62.50
        405 |          1        0.36       62.86
      410.4 |          1        0.36       63.21
     430.56 |          1        0.36       63.57
      439.4 |          1        0.36       63.93
        442 |          1        0.36       64.29
      477.9 |          1        0.36       64.64
     478.17 |          1        0.36       65.00
     480.15 |          1        0.36       65.36
    486.486 |          1        0.36       65.71
     487.62 |          1        0.36       66.07
     498.96 |          1        0.36       66.43
      500.5 |          1        0.36       66.79
      535.5 |          1        0.36       67.14
        540 |          1        0.36       67.50
      542.3 |          1        0.36       67.86
   544.6125 |          1        0.36       68.21
        555 |          1        0.36       68.57
    560.625 |          1        0.36       68.93
     561.33 |          1        0.36       69.29
      564.2 |          1        0.36       69.64
     578.34 |          1        0.36       70.00
   581.3438 |          1        0.36       70.36
      584.1 |          1        0.36       70.71
      595.2 |          1        0.36       71.07
      600.3 |          1        0.36       71.43
     601.12 |          1        0.36       71.79
      602.1 |          1        0.36       72.14
      608.4 |          1        0.36       72.50
     616.86 |          1        0.36       72.86
    642.984 |          1        0.36       73.21
      644.8 |          1        0.36       73.57
      655.5 |          1        0.36       73.93
     660.33 |          1        0.36       74.29
    662.802 |          1        0.36       74.64
      680.4 |          1        0.36       75.00
     689.92 |          1        0.36       75.36
        690 |          1        0.36       75.71
     692.16 |          1        0.36       76.07
      692.3 |          1        0.36       76.43
        715 |          1        0.36       76.79
      716.8 |          1        0.36       77.14
      742.5 |          1        0.36       77.50
        750 |          1        0.36       77.86
    755.055 |          1        0.36       78.21
    755.244 |          1        0.36       78.57
     757.12 |          1        0.36       78.93
     761.53 |          1        0.36       79.29
        770 |          1        0.36       79.64
      770.7 |          1        0.36       80.00
      774.8 |          1        0.36       80.36
      774.9 |          1        0.36       80.71
        775 |          1        0.36       81.07
        792 |          1        0.36       81.43
     792.22 |          1        0.36       81.79
     800.28 |          1        0.36       82.14
    801.528 |          1        0.36       82.50
     805.75 |          1        0.36       82.86
    805.774 |          1        0.36       83.21
      823.5 |          1        0.36       83.57
      842.4 |          1        0.36       83.93
        858 |          1        0.36       84.29
      862.4 |          1        0.36       84.64
        864 |          1        0.36       85.00
    867.955 |          1        0.36       85.36
     899.91 |          1        0.36       85.71
        915 |          1        0.36       86.07
      921.6 |          1        0.36       86.43
    921.875 |          1        0.36       86.79
    922.335 |          1        0.36       87.14
    942.089 |          1        0.36       87.50
     975.24 |          1        0.36       87.86
     994.68 |          1        0.36       88.21
     1020.8 |          1        0.36       88.57
     1054.8 |          1        0.36       88.93
     1065.6 |          1        0.36       89.29
   1066.869 |          1        0.36       89.64
   1077.328 |          1        0.36       90.00
     1094.4 |          1        0.36       90.36
    1095.93 |          1        0.36       90.71
       1101 |          1        0.36       91.07
    1148.85 |          1        0.36       91.43
       1155 |          1        0.36       91.79
    1181.04 |          1        0.36       92.14
       1200 |          1        0.36       92.50
    1203.84 |          1        0.36       92.86
       1215 |          1        0.36       93.21
     1221.3 |          1        0.36       93.57
    1256.32 |          1        0.36       93.93
   1258.125 |          1        0.36       94.29
    1259.55 |          1        0.36       94.64
       1296 |          2        0.71       95.36
    1371.12 |          1        0.36       95.71
       1482 |          1        0.36       96.07
     1769.7 |          1        0.36       96.43
   1834.068 |          1        0.36       96.79
    2073.89 |          1        0.36       97.14
       2160 |          1        0.36       97.50
   2710.396 |          1        0.36       97.86
   2749.107 |          1        0.36       98.21
     3752.5 |          1        0.36       98.57
       3848 |          1        0.36       98.93
       4015 |          1        0.36       99.29
   10840.22 |          1        0.36       99.64
   18275.71 |          1        0.36      100.00
------------+-----------------------------------
      Total |        280      100.00
(15 real changes made)

  Annual PH |
   Chemical |
  Cost (INR |
     000's) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |          3        1.10        1.10
   607.3568 |          1        0.37        1.47
   1594.714 |          1        0.37        1.83
    1989.31 |          1        0.37        2.20
     1993.6 |          1        0.37        2.56
   2215.974 |          1        0.37        2.93
       3144 |          1        0.37        3.30
   3209.733 |          1        0.37        3.66
       5040 |          1        0.37        4.03
       5112 |          1        0.37        4.40
    5293.96 |          1        0.37        4.76
   5620.241 |          1        0.37        5.13
   6427.685 |          1        0.37        5.49
   6600.295 |          1        0.37        5.86
   7100.571 |          1        0.37        6.23
       7332 |          1        0.37        6.59
   8301.458 |          1        0.37        6.96
    8379.25 |          1        0.37        7.33
   8857.886 |          1        0.37        7.69
   9286.022 |          1        0.37        8.06
   9354.384 |          1        0.37        8.42
     9787.5 |          1        0.37        8.79
   11800.03 |          1        0.37        9.16
      12000 |          1        0.37        9.52
   12463.36 |          1        0.37        9.89
   12713.36 |          1        0.37       10.26
   13649.89 |          1        0.37       10.62
   13749.71 |          1        0.37       10.99
   13890.85 |          1        0.37       11.36
    14224.2 |          1        0.37       11.72
   14292.16 |          1        0.37       12.09
   14848.03 |          1        0.37       12.45
   14899.65 |          1        0.37       12.82
   15163.18 |          1        0.37       13.19
    15336.3 |          1        0.37       13.55
      15600 |          1        0.37       13.92
      16968 |          1        0.37       14.29
    17499.6 |          1        0.37       14.65
   17608.59 |          1        0.37       15.02
    19265.4 |          1        0.37       15.38
   19631.04 |          1        0.37       15.75
      19716 |          1        0.37       16.12
    19893.2 |          1        0.37       16.48
    20143.8 |          1        0.37       16.85
   20609.55 |          1        0.37       17.22
   22559.29 |          1        0.37       17.58
      23850 |          1        0.37       17.95
   24799.54 |          1        0.37       18.32
   24839.73 |          1        0.37       18.68
   25056.34 |          1        0.37       19.05
   25746.08 |          1        0.37       19.41
      25840 |          1        0.37       19.78
   26186.15 |          1        0.37       20.15
   26439.28 |          1        0.37       20.51
   26529.86 |          1        0.37       20.88
   26880.21 |          1        0.37       21.25
    27216.2 |          1        0.37       21.61
   27361.13 |          1        0.37       21.98
   27801.39 |          1        0.37       22.34
   28251.51 |          1        0.37       22.71
   28799.68 |          1        0.37       23.08
   28801.74 |          1        0.37       23.44
    28898.1 |          1        0.37       23.81
    29257.8 |          1        0.37       24.18
   29542.43 |          1        0.37       24.54
      30000 |          2        0.73       25.27
   30444.96 |          1        0.37       25.64
    31769.5 |          1        0.37       26.01
    32313.6 |          1        0.37       26.37
    34415.6 |          1        0.37       26.74
   34609.55 |          1        0.37       27.11
   34616.63 |          1        0.37       27.47
      35400 |          1        0.37       27.84
      35510 |          1        0.37       28.21
      35700 |          1        0.37       28.57
      36000 |          2        0.73       29.30
      36366 |          1        0.37       29.67
   36630.71 |          1        0.37       30.04
   37025.01 |          1        0.37       30.40
   39780.21 |          1        0.37       30.77
      40000 |          1        0.37       31.14
   40190.93 |          1        0.37       31.50
   40216.05 |          1        0.37       31.87
   40376.82 |          1        0.37       32.23
   40961.14 |          1        0.37       32.60
    41003.2 |          1        0.37       32.97
      41250 |          1        0.37       33.33
      41305 |          1        0.37       33.70
    41455.5 |          1        0.37       34.07
   42099.45 |          1        0.37       34.43
   42592.13 |          1        0.37       34.80
    42835.5 |          1        0.37       35.16
    43202.5 |          1        0.37       35.53
    43456.9 |          1        0.37       35.90
      44380 |          1        0.37       36.26
   44699.22 |          1        0.37       36.63
   45148.76 |          1        0.37       37.00
   45236.88 |          1        0.37       37.36
      45450 |          1        0.37       37.73
    45530.1 |          1        0.37       38.10
   45866.46 |          1        0.37       38.46
   46546.48 |          1        0.37       38.83
   46630.95 |          1        0.37       39.19
   46702.44 |          1        0.37       39.56
      46800 |          1        0.37       39.93
    46924.8 |          1        0.37       40.29
   47524.18 |          1        0.37       40.66
   47892.99 |          1        0.37       41.03
      49062 |          1        0.37       41.39
   50399.98 |          1        0.37       41.76
   50699.95 |          1        0.37       42.12
      51000 |          1        0.37       42.49
   51003.97 |          1        0.37       42.86
   52392.79 |          1        0.37       43.22
   54043.56 |          1        0.37       43.59
   54359.55 |          1        0.37       43.96
   54950.67 |          1        0.37       44.32
   54998.08 |          1        0.37       44.69
   55439.76 |          1        0.37       45.05
   56114.24 |          1        0.37       45.42
   56810.52 |          1        0.37       45.79
   57284.95 |          1        0.37       46.15
      57600 |          1        0.37       46.52
      57942 |          1        0.37       46.89
      58000 |          1        0.37       47.25
      58976 |          1        0.37       47.62
      59826 |          1        0.37       47.99
   59997.31 |          1        0.37       48.35
   59999.26 |          1        0.37       48.72
   60002.09 |          1        0.37       49.08
      60400 |          1        0.37       49.45
      60610 |          1        0.37       49.82
    60952.5 |          1        0.37       50.18
      61400 |          1        0.37       50.55
   61654.31 |          1        0.37       50.92
   61884.17 |          1        0.37       51.28
    61993.8 |          1        0.37       51.65
      63168 |          1        0.37       52.01
   63598.93 |          1        0.37       52.38
   64192.19 |          1        0.37       52.75
   64524.09 |          1        0.37       53.11
   67317.99 |          1        0.37       53.48
   67770.74 |          1        0.37       53.85
   67830.87 |          1        0.37       54.21
   68085.14 |          1        0.37       54.58
   69973.13 |          1        0.37       54.95
   70202.78 |          1        0.37       55.31
   72795.75 |          1        0.37       55.68
   74371.98 |          1        0.37       56.04
   74542.71 |          1        0.37       56.41
   74878.26 |          1        0.37       56.78
   75091.43 |          1        0.37       57.14
   76170.11 |          1        0.37       57.51
   76496.34 |          1        0.37       57.88
   76500.48 |          1        0.37       58.24
   76534.29 |          1        0.37       58.61
   77240.88 |          1        0.37       58.97
   77498.76 |          1        0.37       59.34
      77994 |          1        0.37       59.71
    79617.8 |          1        0.37       60.07
      80190 |          1        0.37       60.44
   80853.24 |          1        0.37       60.81
   80998.05 |          1        0.37       61.17
    81318.7 |          1        0.37       61.54
   81493.36 |          1        0.37       61.90
   82494.72 |          1        0.37       62.27
   82877.91 |          1        0.37       62.64
   82893.34 |          1        0.37       63.00
    84532.5 |          1        0.37       63.37
      84623 |          1        0.37       63.74
      84700 |          1        0.37       64.10
    88574.2 |          1        0.37       64.47
   90580.84 |          1        0.37       64.84
      92400 |          1        0.37       65.20
    93724.4 |          1        0.37       65.57
    95389.8 |          1        0.37       65.93
   95999.05 |          1        0.37       66.30
   96920.66 |          1        0.37       66.67
   97360.38 |          1        0.37       67.03
   97771.91 |          1        0.37       67.40
   98489.31 |          1        0.37       67.77
    99997.7 |          1        0.37       68.13
     100002 |          1        0.37       68.50
     100506 |          1        0.37       68.86
   101023.9 |          1        0.37       69.23
     101413 |          1        0.37       69.60
   101605.6 |          1        0.37       69.96
   103371.3 |          1        0.37       70.33
   105249.7 |          1        0.37       70.70
   108501.3 |          1        0.37       71.06
   109941.5 |          1        0.37       71.43
   112780.4 |          1        0.37       71.79
   113733.8 |          1        0.37       72.16
   115511.8 |          1        0.37       72.53
   116017.3 |          1        0.37       72.89
     118665 |          1        0.37       73.26
     119790 |          1        0.37       73.63
   119989.9 |          1        0.37       73.99
   119998.9 |          1        0.37       74.36
   120296.3 |          1        0.37       74.73
     120800 |          1        0.37       75.09
   122542.6 |          1        0.37       75.46
   122686.3 |          1        0.37       75.82
   124055.8 |          1        0.37       76.19
   124622.3 |          1        0.37       76.56
   125049.2 |          1        0.37       76.92
     125266 |          1        0.37       77.29
   126841.4 |          1        0.37       77.66
   133537.7 |          1        0.37       78.02
   135095.8 |          1        0.37       78.39
     137500 |          1        0.37       78.75
     137655 |          1        0.37       79.12
   138427.3 |          1        0.37       79.49
   139498.5 |          1        0.37       79.85
   139502.8 |          1        0.37       80.22
   139929.6 |          1        0.37       80.59
     141750 |          1        0.37       80.95
   144028.5 |          1        0.37       81.32
   146439.9 |          1        0.37       81.68
     147500 |          1        0.37       82.05
     150000 |          1        0.37       82.42
   151803.8 |          1        0.37       82.78
   154999.5 |          1        0.37       83.15
   155319.9 |          1        0.37       83.52
   160001.3 |          1        0.37       83.88
   163898.5 |          1        0.37       84.25
   169241.3 |          1        0.37       84.62
   175899.8 |          1        0.37       84.98
   176439.4 |          1        0.37       85.35
   177112.3 |          1        0.37       85.71
   179226.8 |          1        0.37       86.08
   180072.8 |          1        0.37       86.45
   187525.6 |          1        0.37       86.81
   188574.4 |          1        0.37       87.18
   191691.5 |          1        0.37       87.55
   191841.6 |          1        0.37       87.91
   191988.4 |          1        0.37       88.28
   191994.2 |          1        0.37       88.64
   193498.5 |          1        0.37       89.01
   202119.3 |          1        0.37       89.38
   202998.8 |          1        0.37       89.74
   211819.5 |          1        0.37       90.11
   212399.8 |          1        0.37       90.48
     249102 |          1        0.37       90.84
   250087.9 |          1        0.37       91.21
   255860.5 |          1        0.37       91.58
   256083.6 |          1        0.37       91.94
   260608.7 |          1        0.37       92.31
   269680.4 |          1        0.37       92.67
     271716 |          1        0.37       93.04
     292632 |          1        0.37       93.41
   304804.4 |          1        0.37       93.77
   311438.4 |          1        0.37       94.14
   319683.6 |          1        0.37       94.51
   333364.4 |          1        0.37       94.87
     343198 |          1        0.37       95.24
   360017.1 |          1        0.37       95.60
     379200 |          1        0.37       95.97
   382771.3 |          1        0.37       96.34
   409835.6 |          1        0.37       96.70
   429140.4 |          1        0.37       97.07
   600403.1 |          1        0.37       97.44
     889920 |          1        0.37       97.80
   894396.1 |          1        0.37       98.17
    1491164 |          1        0.37       98.53
    2861181 |          1        0.37       98.90
    8880601 |          1        0.37       99.27
   1.20e+07 |          1        0.37       99.63
   1.20e+08 |          1        0.37      100.00
------------+-----------------------------------
      Total |        273      100.00
(22 real changes made)

 Annual ETP |
   Chemical |
  Cost (INR |
     000's) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |          3        1.64        1.64
       30.1 |          1        0.55        2.19
     30.702 |          1        0.55        2.73
      32.76 |          1        0.55        3.28
     34.848 |          1        0.55        3.83
      38.07 |          1        0.55        4.37
     47.724 |          1        0.55        4.92
      50.05 |          1        0.55        5.46
       60.2 |          2        1.09        6.56
         63 |          1        0.55        7.10
      67.68 |          1        0.55        7.65
     70.452 |          1        0.55        8.20
      72.96 |          1        0.55        8.74
       82.5 |          1        0.55        9.29
       83.7 |          1        0.55        9.84
      89.64 |          1        0.55       10.38
     90.072 |          1        0.55       10.93
       90.3 |          1        0.55       11.48
       91.8 |          1        0.55       12.02
     92.664 |          1        0.55       12.57
         93 |          1        0.55       13.11
      95.68 |          1        0.55       13.66
         96 |          1        0.55       14.21
        108 |          1        0.55       14.75
     110.95 |          1        0.55       15.30
        114 |          1        0.55       15.85
      117.6 |          1        0.55       16.39
        120 |          1        0.55       16.94
     120.05 |          1        0.55       17.49
   121.1636 |          1        0.55       18.03
      124.8 |          3        1.64       19.67
      125.6 |          1        0.55       20.22
      130.5 |          1        0.55       20.77
      134.1 |          1        0.55       21.31
     143.55 |          1        0.55       21.86
      147.5 |          1        0.55       22.40
      148.8 |          1        0.55       22.95
     151.25 |          1        0.55       23.50
      157.2 |          1        0.55       24.04
        159 |          1        0.55       24.59
      159.6 |          1        0.55       25.14
      162.6 |          1        0.55       25.68
   166.6283 |          1        0.55       26.23
        171 |          1        0.55       26.78
      171.6 |          1        0.55       27.32
        175 |          1        0.55       27.87
        177 |          1        0.55       28.42
        180 |          1        0.55       28.96
    184.375 |          1        0.55       29.51
   186.5587 |          1        0.55       30.05
        189 |          1        0.55       30.60
     191.25 |          1        0.55       31.15
      193.6 |          1        0.55       31.69
      202.5 |          1        0.55       32.24
        204 |          1        0.55       32.79
     210.45 |          1        0.55       33.33
      210.7 |          1        0.55       33.88
      212.1 |          1        0.55       34.43
      229.6 |          1        0.55       34.97
      231.2 |          1        0.55       35.52
      236.6 |          1        0.55       36.07
     237.15 |          1        0.55       36.61
      246.5 |          1        0.55       37.16
   246.9589 |          1        0.55       37.70
      249.6 |          1        0.55       38.25
      255.2 |          1        0.55       38.80
        256 |          1        0.55       39.34
   264.8251 |          1        0.55       39.89
        270 |          2        1.09       40.98
        300 |          2        1.09       42.08
      303.3 |          1        0.55       42.62
     311.85 |          1        0.55       43.17
    315.504 |          1        0.55       43.72
      325.5 |          1        0.55       44.26
     340.48 |          1        0.55       44.81
      343.2 |          1        0.55       45.36
      347.6 |          1        0.55       45.90
        351 |          1        0.55       46.45
   351.1404 |          1        0.55       46.99
    352.184 |          1        0.55       47.54
      352.8 |          1        0.55       48.09
   355.9706 |          1        0.55       48.63
      362.4 |          1        0.55       49.18
      387.8 |          1        0.55       49.73
      388.8 |          1        0.55       50.27
   405.8194 |          1        0.55       50.82
        408 |          1        0.55       51.37
        417 |          1        0.55       51.91
    417.725 |          1        0.55       52.46
        435 |          1        0.55       53.01
      437.5 |          1        0.55       53.55
      445.3 |          1        0.55       54.10
        448 |          1        0.55       54.64
        450 |          1        0.55       55.19
        453 |          1        0.55       55.74
        459 |          1        0.55       56.28
        465 |          1        0.55       56.83
      472.5 |          1        0.55       57.38
      476.8 |          1        0.55       57.92
      499.2 |          1        0.55       58.47
   507.6864 |          1        0.55       59.02
        512 |          1        0.55       59.56
     516.25 |          1        0.55       60.11
        540 |          1        0.55       60.66
        551 |          1        0.55       61.20
    551.769 |          1        0.55       61.75
      553.8 |          1        0.55       62.30
      566.2 |          1        0.55       62.84
      577.5 |          1        0.55       63.39
    584.375 |          1        0.55       63.93
        590 |          1        0.55       64.48
      592.8 |          1        0.55       65.03
     596.25 |          1        0.55       65.57
        600 |          1        0.55       66.12
        602 |          1        0.55       66.67
     603.75 |          1        0.55       67.21
      635.8 |          1        0.55       67.76
    675.675 |          1        0.55       68.31
     681.75 |          1        0.55       68.85
     686.34 |          1        0.55       69.40
    694.884 |          1        0.55       69.95
        720 |          1        0.55       70.49
      748.8 |          1        0.55       71.04
        780 |          1        0.55       71.58
    780.087 |          1        0.55       72.13
      837.2 |          1        0.55       72.68
      892.5 |          1        0.55       73.22
        900 |          1        0.55       73.77
        906 |          1        0.55       74.32
    909.675 |          1        0.55       74.86
      913.5 |          1        0.55       75.41
        936 |          1        0.55       75.96
        987 |          1        0.55       76.50
      999.9 |          1        0.55       77.05
     1053.5 |          1        0.55       77.60
       1071 |          1        0.55       78.14
     1072.8 |          1        0.55       78.69
     1076.4 |          1        0.55       79.23
    1088.85 |          1        0.55       79.78
     1216.8 |          1        0.55       80.33
    1235.08 |          1        0.55       80.87
       1248 |          1        0.55       81.42
   1286.174 |          1        0.55       81.97
    1326.65 |          1        0.55       82.51
     1348.5 |          1        0.55       83.06
       1500 |          1        0.55       83.61
       1518 |          1        0.55       84.15
       1584 |          1        0.55       84.70
    1621.62 |          1        0.55       85.25
       1782 |          1        0.55       85.79
       1800 |          1        0.55       86.34
     2047.5 |          1        0.55       86.89
       2142 |          1        0.55       87.43
     2152.5 |          1        0.55       87.98
    2179.16 |          1        0.55       88.52
     2582.1 |          1        0.55       89.07
     2623.5 |          1        0.55       89.62
       2907 |          1        0.55       90.16
   3332.875 |          1        0.55       90.71
       3627 |          1        0.55       91.26
   3749.715 |          1        0.55       91.80
    3979.92 |          1        0.55       92.35
   4725.425 |          1        0.55       92.90
   4835.148 |          1        0.55       93.44
       5220 |          1        0.55       93.99
       5328 |          1        0.55       94.54
    6319.53 |          1        0.55       95.08
       7128 |          1        0.55       95.63
   8065.394 |          1        0.55       96.17
   8997.972 |          1        0.55       96.72
     9307.2 |          1        0.55       97.27
   23002.43 |          1        0.55       97.81
   63816.63 |          1        0.55       98.36
   74720.72 |          1        0.55       98.91
   173381.6 |          1        0.55       99.45
   266695.5 |          1        0.55      100.00
------------+-----------------------------------
      Total |        183      100.00
(112 real changes made)

  Annual BH |
   Chemical |
  Cost (INR |
     000's) |      Freq.     Percent        Cum.
------------+-----------------------------------
       14.4 |          2        0.74        0.74
         15 |          1        0.37        1.12
         18 |          1        0.37        1.49
      27.72 |          1        0.37        1.86
         30 |          2        0.74        2.60
         36 |          2        0.74        3.35
         42 |          1        0.37        3.72
       43.2 |          1        0.37        4.09
         48 |          5        1.86        5.95
       52.8 |          1        0.37        6.32
         54 |          1        0.37        6.69
       54.6 |          1        0.37        7.06
       59.4 |          1        0.37        7.43
         60 |          8        2.97       10.41
       63.6 |          1        0.37       10.78
         66 |          1        0.37       11.15
      68.64 |          1        0.37       11.52
       70.8 |          2        0.74       12.27
         72 |          9        3.35       15.61
         78 |          1        0.37       15.99
         81 |          1        0.37       16.36
         84 |          8        2.97       19.33
      88.92 |          1        0.37       19.70
       91.2 |          1        0.37       20.07
      94.08 |          1        0.37       20.45
         96 |         15        5.58       26.02
        102 |          1        0.37       26.39
      105.6 |          1        0.37       26.77
     106.68 |          1        0.37       27.14
     107.04 |          1        0.37       27.51
        108 |          7        2.60       30.11
        114 |          3        1.12       31.23
        120 |         20        7.43       38.66
      124.8 |          1        0.37       39.03
        132 |          3        1.12       40.15
   134.5306 |          1        0.37       40.52
    135.936 |          1        0.37       40.89
        138 |          1        0.37       41.26
        144 |         18        6.69       47.96
        150 |          2        0.74       48.70
      151.2 |          1        0.37       49.07
        156 |          5        1.86       50.93
      159.3 |          1        0.37       51.30
        162 |          1        0.37       51.67
        168 |          2        0.74       52.42
      175.5 |          1        0.37       52.79
    179.244 |          1        0.37       53.16
        180 |         17        6.32       59.48
        186 |          2        0.74       60.22
        192 |          3        1.12       61.34
        204 |          3        1.12       62.45
    214.032 |          1        0.37       62.83
        216 |         11        4.09       66.91
        222 |          2        0.74       67.66
        228 |          1        0.37       68.03
        234 |          1        0.37       68.40
        240 |         20        7.43       75.84
      244.5 |          1        0.37       76.21
        252 |          1        0.37       76.58
        264 |          1        0.37       76.95
        270 |          1        0.37       77.32
        276 |          2        0.74       78.07
      280.8 |          1        0.37       78.44
        288 |          1        0.37       78.81
        300 |          9        3.35       82.16
        312 |          3        1.12       83.27
        324 |          1        0.37       83.64
        360 |          6        2.23       85.87
        378 |          1        0.37       86.25
        396 |          1        0.37       86.62
        420 |          3        1.12       87.73
        432 |          1        0.37       88.10
        450 |          1        0.37       88.48
        480 |          1        0.37       88.85
        492 |          1        0.37       89.22
      499.2 |          1        0.37       89.59
      501.6 |          1        0.37       89.96
        528 |          1        0.37       90.33
        600 |          5        1.86       92.19
      600.3 |          1        0.37       92.57
        660 |          2        0.74       93.31
        696 |          1        0.37       93.68
      717.6 |          1        0.37       94.05
        720 |          1        0.37       94.42
        744 |          1        0.37       94.80
      767.1 |          1        0.37       95.17
        768 |          1        0.37       95.54
        780 |          2        0.74       96.28
        816 |          1        0.37       96.65
        900 |          1        0.37       97.03
        936 |          1        0.37       97.40
       1068 |          1        0.37       97.77
       1332 |          1        0.37       98.14
       1380 |          1        0.37       98.51
     1502.4 |          1        0.37       98.88
       1560 |          1        0.37       99.26
       2400 |          1        0.37       99.63
       2460 |          1        0.37      100.00
------------+-----------------------------------
      Total |        269      100.00
(26 real changes made)

.                 
.                 * truncate high values
.                 tabstat ph_tot_chem_cost , stat(mean p50) save

    Variable |      Mean       p50
-------------+--------------------
ph_tot_che~t |  578374.2  57284.95
----------------------------------

.                 matrix temp = r(StatTotal)

.                 matrix list temp

temp[2,1]
      ph_tot_che~t
Mean     578374.17
 p50     57284.949

.                 local impute temp[2,1]

.                 di `impute'
57284.949

.                 replace ph_tot_chem_cost = `impute' if ph_tot_chem_cost > (10
> 000*annu_work_days) & ph_tot_chem_cost != .
(3 real changes made)

.                 
.                 * create aggregate variable
.                 gen plant_cost_material = ph_tot_rawmtrl_cost + ph_tot_water_
> cost/100 + bh_annu_water_cost/100 + ///
>                         ph_tot_chem_cost/100 + tot_chem_cost_etp/100 + bh_ann
> u_chem_cost/100

.                 label var plant_cost_material "Total Material Cost (INR Lakhs
> )"

.                 
.                 * replace original values back to normal
.                 *foreach var of varlist ph_tot_rawmtrl_cost ph_tot_water_cost
>  bh_annu_water_cost ///
>                 *ph_tot_chem_cost tot_chem_cost_etp bh_annu_chem_cost {
.                 *       replace `var' = . if `var' == 0
.                 *}
.                 
. //      Rename relevant BH costs 
. 
.         gen bh_cost_capital = plant_cost_capital
(68 missing values generated)

.         label var bh_cost_capital  "BH Capital (INR Lakhs)"

.         
.         gen bh_cost_labor = bh_annu_labor_cost + bh_engineer_cost + bh_an_mas
> ter_cost
(16 missing values generated)

.         label var bh_cost_labor  "BH Labor Cost (INR Lakhs)"

.         
.         gen bh_cost_elec = bh_annu_elec_cost

.         label var bh_cost_elec  "BH Electricity Cost (INR Lakhs)"

.         
.         gen bh_cost_fuel = total_fuel_cost_1920
(48 missing values generated)

.         label var bh_cost_fuel  "BH Fuel Cost (INR Lakhs)"

.         
.         gen bh_cost_fuel_bl = total_fuel_cost_1819
(58 missing values generated)

.         label var bh_cost_fuel_bl  "BH Fuel Cost (INR Lakhs) - Baseline"

.         
.         gen bh_cost_material = bh_annu_water_cost/100 + bh_annu_chem_cost/100

.         label var bh_cost_material "BH Material Cost (INR Lakhs)"

.         
. //      Edit plant costs to non-BH costs; except elec (we will report only pl
> ant)
.         replace plant_cost_capital = plant_cost_capital - bh_cost_capital
(227 real changes made)

.         replace plant_cost_labor = plant_cost_labor - bh_cost_labor
(266 real changes made)

.         replace plant_cost_material = plant_cost_material - bh_cost_material 
(273 real changes made)

.         replace plant_cost_fuel = plant_cost_fuel - bh_cost_fuel
(247 real changes made)

.         
. //      Keep relevant variables, rename to signal endline
.         keep industry_id plant_cost_* bh_cost_* num_cyclones num_bagfilters n
> um_scrubbers num_esps D_cyc D_bf D_scr D_esp

.         
.         ds industry_id *_bl, not
num_cyclones  D_cyc         plant_co~tal  plant_co~ial  bh_cost_fuel
num_bagfil~s  D_bf          plant_cost~r  bh_cost_ca~l  bh_cost_ma~l
num_scrubb~s  D_scr         plant_cos~el  bh_cost_la~r
num_esps      D_esp         plant_cost~c  bh_cost_elec

.         foreach var of varlist `r(varlist)' {
  2.                 rename `var' `var'_el
  3.         }

.         
. //      Save to temp file
.         tempfile data

.         save `data', replace
(file /var/folders/7r/1zsrb09s0r578bq3nysvhzd40000gn/T//St40523.000001 not
    found)
file /var/folders/7r/1zsrb09s0r578bq3nysvhzd40000gn/T//St40523.000001 saved
    as .dta format

.                                         
. **********************************************************************
. ******          Merge Factors of Production with Baseline Values         ****
> *
. **********************************************************************
. 
. use "$BASELINE_DATA_IN/BaselineCovariates_318i.dta", clear
(BASELINE COVARS (304i). +APCMS. +STACKSAMP. +JUNECEMS. +RINGEL. +XGN. 318 PLAN
> TS)

. 
.         label var bh_annu_ope_cost_lakh "Annual BH Operating Cost (INR Lakhs)
> "

.         label var bh_annu_maint_cost_lakh "Annual BH Maintenance Cost (INR La
> khs)"

.         label var bh_annu_labor_cost  "BH Annual Worker Cost (INR Lakhs)"

.         label var bh_annu_elec_cost "BH Electricity Cost"

.         label var bh_annu_inputs_cost "BH Inputs Cost"

.         label var treat "ETS Treatment=1"

. 
. //      Generate key aggregate plant-level variables --> these are for regres
> sion controls      
.         
.         * Total Revenue
.                 rename grossrev_17_18_clean plant_rev

.                 replace plant_rev = plant_rev*100 // convert from crore to la
> khs
(290 real changes made)

.                 label var plant_rev "Total Revenue 2017 (INR Lakhs) - Baselin
> e"

. 
.         * Capital 
.                 * set missing values to 0
.                 foreach var of varlist bh_total_smallrepair bh_annu_ope_cost_
> lakh bh_annu_maint_cost_lakh {
  2.                         replace `var' = 0 if `var' == .
  3.                 }
(14 real changes made)
(14 real changes made)
(14 real changes made)

.                 * create aggregate variable
.                 gen working_capital = bh_total_smallrepair + bh_annu_ope_cost
> _lakh + bh_annu_maint_cost_lakh 

.                 label var working_capital  "Working Capital (INR Lakhs) - Bas
> eline"

.                 * replace 0 values with missing again
.                 foreach var of varlist bh_total_smallrepair bh_annu_ope_cost_
> lakh bh_annu_maint_cost_lakh {
  2.                         replace `var' = . if `var' == 0
  3.                 }
(23 real changes made, 23 to missing)
(14 real changes made, 14 to missing)
(15 real changes made, 15 to missing)

.         
.                 * Total (= working capital, bc no fixed capital in baseline)
.                 gen plant_cost_capital = working_capital

.                 label var plant_cost_capital  "Total Capital (INR Lakhs) - Ba
> seline"

.                 
.         * Labor Costs
.                 gen plant_cost_labor = bh_annu_labor_cost // we use bh as con
> trol
(14 missing values generated)

.                 label var plant_cost_labor  "Total Labor Cost (INR Lakhs) - B
> aseline"

.                 
.         * Electricity Costs
.                 gen plant_cost_elec = elecbill_2017
(30 missing values generated)

.                 label var plant_cost_elec  "Total Electricity Cost (INR Lakhs
> ) - Baseline"

.                 
.         * Fuel Costs (= pulled from phone survey)
.                 
.         * Material Costs
.                 gen plant_cost_material = (annu_work_days * bh_water_cost_per
> day + 12 * bh_month_chem_cost)/100000
(14 missing values generated)

.                 label var plant_cost_material  "Total Material Cost (INR Lakh
> s) - Baseline"

.         
. //      Rename relevant BH costs --> these are for regression controls
. 
.         gen bh_cost_capital = plant_cost_capital

.         label var bh_cost_capital  "BH Capital (INR Lakhs) - Baseline"

.         
.         gen bh_cost_labor = bh_annu_labor_cost
(14 missing values generated)

.         label var bh_cost_labor  "BH Labor Cost (INR Lakhs) - Baseline"

.         
.         gen bh_cost_elec = bh_annu_elec_cost
(14 missing values generated)

.         label var bh_cost_elec  "BH Electricity Cost (INR Lakhs) - Baseline"

.         
.         ** fuel from phone survey
.         
.         gen bh_cost_material = bh_annu_inputs_cost
(14 missing values generated)

.         label var bh_cost_material "BH Material Cost (INR Lakhs) - Baseline" 
>    

.         
.         
. //      Keep relevant variables, rename to signal baseline
.         rename D_treatment treat

.         label var treat "ETS Treatment=1"

.         keep industry_id treat plant_cost_* bh_cost_* ///
>                 num_cyclones num_bagfilters num_scrubbers num_esps D_cyc D_bf
>  D_scr D_esp ///
>                 plant_boi_cap

.                 
.         ds industry_id plant_boi_cap treat, not
num_cyclones  D_cyc         plant_co~tal  bh_cost_ca~l
num_scrubb~s  D_scr         plant_cost~r  bh_cost_la~r
num_bagfil~s  D_bf          plant_cost~c  bh_cost_elec
num_esps      D_esp         plant_co~ial  bh_cost_ma~l

.         foreach var of varlist `r(varlist)' {
  2.                 rename `var' `var'_bl
  3.         }

.         
. *****************************************
. **************** Merge ******************
. *****************************************
. 
.         sort industry_id

.         merge 1:1 industry_id using `data'

    Result                      Number of obs
    -----------------------------------------
    Not matched                            23
        from master                        23  (_merge==1)
        from using                          0  (_merge==2)

    Matched                               295  (_merge==3)
    -----------------------------------------

.         assert _m!=2

.         
.         gen d_onlybl = (_m==1)

.         quietly sum d_onlybl

.         assert `r(sum)'==23

.         drop _m

.         
. *****************************************
. ****** Additional Data Processing  ******
. *****************************************
. 
. // Top code material costs at 99th percentile within treatment arm, due to ou
> tlier
. 
. ds *cost_material_*
plant~ial_bl  bh_cost_m~bl  plant~ial_el  bh_cost_m~el

. foreach var of varlist `r(varlist)' {
  2.         foreach status in 0 1 {
  3.                 quietly: sum `var' if treat==`status', d
  4.                 replace `var' = `r(p99)' if `var' > `r(p99)' & treat==`sta
> tus'
  5.         }
  6. }
(10 real changes made)
(6 real changes made)
(10 real changes made)
(6 real changes made)
(15 real changes made)
(10 real changes made)
(15 real changes made)
(10 real changes made)

. 
. //      Convert number of Abatement Devices to costs
.         ** costs pulled from this spreadsheet: https://docs.google.com/spread
> sheets/d/1UNsdN85sn891vtabzV8pF1T76U5445cs5ZPWwV2OPDE/edit#gid=0
.         ** (a) Why do 14 plants have 0 boiler cap? --> powered by TPH and HAG
> s 
.         ** (b) Why does one plant (GJS_334) have 183 boiler cap? --> accurate
> , it's just a large plant
.         ** follow-up question: how to impute costs for these cases?  We set 0
>  to missing, and group 183 into the largest bucket.
.         
.         * create an indicator for boiler cap thresholds per spreadsheet
.         gen plant_boi_cap_size = 0 

.         replace plant_boi_cap_size = 1 if plant_boi_cap >= 0 & plant_boi_cap 
> < 4 // spreadsheet said lower bound is 1, but we have 0s
(49 real changes made)

.         replace plant_boi_cap_size = 2 if plant_boi_cap >= 4 & plant_boi_cap 
> < 8
(142 real changes made)

.         replace plant_boi_cap_size = 3 if plant_boi_cap >= 8 & plant_boi_cap 
> < 15
(109 real changes made)

.         replace plant_boi_cap_size = 4 if plant_boi_cap >= 15 & plant_boi_cap
>  < 25
(12 real changes made)

.         replace plant_boi_cap_size = 5 if plant_boi_cap >= 25 & plant_boi_cap
>  < 185 // spreadsheet said upper bound is 90, but we have one 183 value
(6 real changes made)

.         
.         * spreadsheet implies large boilers should have no bagfilters and no 
> scrubbers
.         ds num_bagfilters_* num_scrubbers_*
num_bagfi~bl  num_bagfi~el  num_scrub~bl  num_scrub~el

.         foreach var in `r(varlist)' {
  2.                 assert `var'==0 if plant_boi_cap_size==5
  3.         }

.         
.         * import spreadsheet cost matrix (unit: INR Lakhs)
.         local cost_cyclones 4 5.5 6.5 8 8

.         local cost_bagfilters 3.75 9 11.5 12 0 

.         local cost_scrubbers 6 9.5 15 15 0

.         local cost_esps 45 60 100 135 225

.         
.         * impute costs based on cost matrix
.         foreach apcd in cyclones bagfilters scrubbers esps {
  2.                 
.                 * initialize cost variables
.                 gen `apcd'_unit_cost = 0
  3.                 
.                 * loop over each apcd and all five costs
.                 local i = 1
  4.                 foreach val in `cost_`apcd'' {
  5.                         replace `apcd'_unit_cost = `val' if plant_boi_cap_
> size==`i' 
  6.                         local i = `i'+1
  7.                 }
  8.                 
.         }
(49 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(6 real changes made)
(49 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(0 real changes made)
(49 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(0 real changes made)
(49 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(6 real changes made)

.         drop plant_boi_cap plant_boi_cap_size

.         
.         * create total apcd cost per plant
.         foreach apcd in cyclones bagfilters scrubbers esps {
  2.                 gen `apcd'_total_cost_el = num_`apcd'_el * `apcd'_unit_cos
> t
  3.                 gen `apcd'_total_cost_bl = num_`apcd'_bl * `apcd'_unit_cos
> t
  4.         }
(23 missing values generated)
(14 missing values generated)
(23 missing values generated)
(14 missing values generated)
(23 missing values generated)
(14 missing values generated)
(23 missing values generated)
(14 missing values generated)

.         
.         label var cyclones_unit_cost "Cyclones Unit Cost (INR Lakhs)"

.         label var bagfilters_unit_cost "Bag Filters Unit Cost (INR Lakhs)"

.         label var scrubbers_unit_cost "Scrubbers Unit Cost (INR Lakhs)"

.         label var esps_unit_cost "ESPs Unit Cost (INR Lakhs)"

.         label var cyclones_total_cost_el "Cyclones Total Cost (INR Lakhs)"

.         label var cyclones_total_cost_bl "Cyclones Total Cost (INR Lakhs) - B
> aseline"

.         label var bagfilters_total_cost_el "Bag Filters Total Cost (INR Lakhs
> )"

.         label var bagfilters_total_cost_bl "Bag Filters Total Cost (INR Lakhs
> ) - Baseline"

.         label var scrubbers_total_cost_el "Scrubbers Total Cost (INR Lakhs)"

.         label var scrubbers_total_cost_bl "Scrubbers Total Cost (INR Lakhs) -
>  Baseline"

.         label var esps_total_cost_el "ESPs Total Cost (INR Lakhs)"

.         label var esps_total_cost_bl "ESPs Total Cost (INR Lakhs) - Baseline"

.         
. //      Turn all costs into USD thousands 
.         ds *_cost_* *_cost
plant~tal_bl  bh_cost_m~bl  bh_cost_c~el  cyclones_~bl  cyclones_u~t
plant_c~r_bl  plant~tal_el  bh_cost_l~el  bagfilter~el  bagfilters~t
plant_c~c_bl  plant_c~r_el  bh_cost_e~el  bagfilter~bl  scrubbers_~t
plant~ial_bl  plant_~el_el  bh_cost_f~el  scrubbers~el  esps_unit_~t
bh_cost_c~bl  plant_~el_bl  bh_cost_f~bl  scrubbers~bl
bh_cost_l~bl  plant_c~c_el  bh_cost_m~el  esps_tota~el
bh_cost_e~bl  plant~ial_el  cyclones_~el  esps_tota~bl

.         foreach var in `r(varlist)' {
  2.                 replace `var' = `var'*$USD2INR/100
  3.         }
(304 real changes made)
(304 real changes made)
(287 real changes made)
(291 real changes made)
(304 real changes made)
(304 real changes made)
(304 real changes made)
(291 real changes made)
(0 real changes made)
(266 real changes made)
(0 real changes made)
(237 real changes made)
(267 real changes made)
(305 real changes made)
(227 real changes made)
(278 real changes made)
(294 real changes made)
(247 real changes made)
(237 real changes made)
(296 real changes made)
(286 real changes made)
(297 real changes made)
(250 real changes made)
(252 real changes made)
(196 real changes made)
(189 real changes made)
(38 real changes made)
(30 real changes made)
(318 real changes made)
(312 real changes made)
(312 real changes made)
(318 real changes made)

. 
. //      Only keep plants in endline
.         drop if d_onlybl==1
(23 observations deleted)

.         drop d_onlybl   

. 
. //      Save to temp file
.         tempfile data

.         save `data', replace
(file /var/folders/7r/1zsrb09s0r578bq3nysvhzd40000gn/T//St40523.000002 not
    found)
file /var/folders/7r/1zsrb09s0r578bq3nysvhzd40000gn/T//St40523.000002 saved
    as .dta format

.         
. *****************************************
. ******* Filter to Analysis Panel  *******
. *****************************************
. 
. use "$EMISSIONS_DATA_OUT/RuleA_Panel.dta", clear
(337 STACKS. MASTER T&C BALANCED PANEL. HIST CALIB FACTORS.)

. keep industry_id

. quietly bysort industry_id:  gen dup = cond(_N==1,0,_n)

. drop if dup>1
(6,716 observations deleted)

. drop dup

. 
. * only matched with 283 of the 292 analysis plants
. merge 1:1 industry_id using `data', keep(match) nogenerate

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               283  
    -----------------------------------------

. 
. save "$PHONE_DATA_OUT/PhoneBaseline.dta", replace
(file 02DataPipeline/phone_survey/PhoneBaseline.dta not found)
file 02DataPipeline/phone_survey/PhoneBaseline.dta saved

. 
end of do-file

. 
. ** First Step structural
. do "$CODE_DIR/prepare_structural_model_data_firststep.do"

. /*********************************************************************
> Purpose: Merge and prepare phone survey and baseline covariates for analysis.
> *********************************************************************/
. 
. clear all

. clear matrix

. set more off

. set linesize 255

. pause on

. 
. 
. use "$BASELINE_DATA_OUT/BaselineCovariates.dta", clear  
('IN-SAMPLE' ETS MASTER. 318 INDS. +TREATMENT)

. 
. //      Generate variables for APCD costs
. ** Capital costs pulled from this spreadsheet: https://docs.google.com/spreadsheets/d/1UNsdN85sn891vtabzV8pF1T76U5445cs5ZPWwV2OPDE/edit#gid=0
. ** (a) Why do 14 plants have 0 boiler cap? --> powered by TPH and HAGs (b) Why does one plant (GJS_334) have 183 boiler cap? --> accurate
. ** follow-up question: how to impute costs for these cases? We set 0 to missing, and group 183 into the largest bucket.
. 
. * create an indicator for boiler cap thresholds per spreadsheet
. gen plant_boi_cap_size = 0

. replace plant_boi_cap_size = . if plant_boi_cap == 0
(16 real changes made, 16 to missing)

. replace plant_boi_cap_size = 1 if plant_boi_cap > 0 & plant_boi_cap < 4 // spreadsheet said lower bound is 1, leave 0s as missing
(33 real changes made)

. replace plant_boi_cap_size = 2 if plant_boi_cap >= 4 & plant_boi_cap < 8
(142 real changes made)

. replace plant_boi_cap_size = 3 if plant_boi_cap >= 8 & plant_boi_cap < 15
(109 real changes made)

. replace plant_boi_cap_size = 4 if plant_boi_cap >= 15 & plant_boi_cap < 25
(12 real changes made)

. replace plant_boi_cap_size = 5 if plant_boi_cap >= 25 & plant_boi_cap < 185 // spreadsheet said upper bound is 90, but we have one 183 value
(6 real changes made)

. 
. tab plant_boi_cap_size // size 1: 33, size 2: 142, size 3: 109, size 4: 12, size 5: 6. 

plant_boi_c |
    ap_size |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         33       10.93       10.93
          2 |        142       47.02       57.95
          3 |        109       36.09       94.04
          4 |         12        3.97       98.01
          5 |          6        1.99      100.00
------------+-----------------------------------
      Total |        302      100.00

. 
. * import spreadsheet cost matrix
. local cost_cyclone 4 5.5 6.5 8 8

. local cost_bagfilter 3.75 9 11.5 12 . 

. local cost_scrubber 6 9.5 15 15 .

. local cost_esp 45 60 100 135 225

. 
. * impute costs based on cost matrix
. foreach apcd in cyclone bagfilter scrubber esp {
  2. 
.         * initialize cost variables
.         gen apcd_unitcost_install_`apcd' = .
  3. 
.         * loop over each apcd and all five costs
.         local i = 1
  4.         foreach val in `cost_`apcd'' {
  5.                 replace apcd_unitcost_install_`apcd' = `val' if plant_boi_cap_size==`i' 
  6.                 label var apcd_unitcost_install_`apcd' "Unit Installation Cost for each `apcd' (Rs)"
  7.                 local i = `i'+1
  8.         }
  9. }
(318 missing values generated)
(33 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(6 real changes made)
(318 missing values generated)
(33 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(0 real changes made)
(318 missing values generated)
(33 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(0 real changes made)
(318 missing values generated)
(33 real changes made)
(142 real changes made)
(109 real changes made)
(12 real changes made)
(6 real changes made)

. 
. // Create placeholders for maint and var costs, until we get them
. // Jahnavi (previously Rohini's PhD student) said FICCI suggested using 3% and 6% resp. for operations and maintenance costs. See appendix Table C.3 in her dissertation:
. *https://dash.harvard.edu/bitstream/handle/1/40050139/NILEKANI-DISSERTATION-2018.pdf?sequence=4
. 
. foreach apcd in cyclone bagfilter scrubber esp {
  2.         * generate variables for APCD maintenance costs
.         gen apcd_unitcost_maint_`apcd' = 0.06 * apcd_unitcost_install_`apcd'
  3.         label var apcd_unitcost_maint_`apcd' "Maintenance Cost (annual) for each `apcd' (Rs)"
  4.         * generate variables for APCD operating costs
.         gen apcd_unitcost_ope_`apcd' = 0.03 * apcd_unitcost_install_`apcd'
  5.         label var apcd_unitcost_ope_`apcd' "Operating Cost (annual) for each `apcd' (Rs)"
  6. }
(16 missing values generated)
(16 missing values generated)
(22 missing values generated)
(22 missing values generated)
(22 missing values generated)
(22 missing values generated)
(16 missing values generated)
(16 missing values generated)

. 
. keep ///
>         gpcb_id /// Unique plant ID
>         industry_name /// Plant name
>         treatmentstatus /// Treatment status
>         plant_total_heatoutput /// Total heat output (boiler tph equivalent)
>         pm_conc_etsbl /// Baseline emissions concentration (mg/Nm3)
>         pm_mass_etsbl /// [**per hour] Baseline emissions load (kg/hr) (where available in pre-experiment period)
>         D_cyc D_bf D_scr D_esp /// APCD installation dummies as of baseline: Cyclone, bag filter, scrubber, ESP 
>         cyc_max bf_max scr_esp_max /// Maximal APCD device
>         apcd_unitcost_install_* /// APCD capital costs for each equipment (from engineering estimates)
>         num_cyclones num_bagfilters num_scrubbers num_esps /// 
>         apcd_unitcost_maint_*  /// APCD maintenance costs for each equipment (from engineering estimates as 6% of installation)
>         apcd_unitcost_ope_* // APCD operating costs for each equipment (from engineering estimates as 3% of installation)

. 
. // Convert costs into lakh rupees
. ds *_unitcost_*
ap~l_cyclone  ~l_bagfilter  a~l_scrubber  apcd_u~l_esp  ap~t_cyclone  ap~e_cyclone  ~t_bagfilter  ~e_bagfilter  a~t_scrubber  a~e_scrubber  apcd_u~t_esp  apcd_u~e_esp

. foreach var in `r(varlist)' {
  2.         replace `var' = 100000*`var'
  3. }
(302 real changes made)
(296 real changes made)
(296 real changes made)
(302 real changes made)
(302 real changes made)
(302 real changes made)
(296 real changes made)
(296 real changes made)
(296 real changes made)
(296 real changes made)
(302 real changes made)
(302 real changes made)

. 
. // Rename and label variables for output
. rename industry_name gpcb_name

. label var gpcb_id "Unique plant ID"

. gen D_treatment = (treatmentstatus=="Treatment")

. label var D_treatment "Plant treatment status"

. drop treatmentstatus

. 
. rename plant_total_heatoutput heatoutput

. rename pm_conc_etsbl emissions_conc_etsbl

. rename pm_mass_etsbl emissions_mass_etsbl

. 
. rename D_cyc apcd_present_cyclone

. rename D_bf apcd_present_bagfilter

. rename D_scr apcd_present_scrubber

. rename D_esp apcd_present_esp

. 
. rename cyc_max apcd_maximal_cyclone

. rename bf_max apcd_maximal_bagfilter

. rename scr_esp_max apcd_maximal_scrubber_esp

. 
. rename num_cyclones apcd_count_cyclone

. rename num_bagfilters apcd_count_bagfilter

. rename num_scrubbers apcd_count_scrubber

. rename num_esps apcd_count_esp

. 
. order gpcb_id gpcb_name D_treatment heatoutput emissions_conc_etsbl emissions_mass_etsbl apcd_present_* apcd_maximal_* apcd_count_* apcd_unitcost_install_* apcd_unitcost_maint_* apcd_unitcost_ope_* 

. 
. tempfile data

. save `data'
file /var/folders/7r/1zsrb09s0r578bq3nysvhzd40000gn/T//St40523.000001 saved as .dta format

. 
. //      Create dummy if plant is in analysis panel
. 
. use "$EMISSIONS_DATA_OUT/Rule0_Panel.dta", clear
(337 STACKS. MASTER T&C BALANCED PANEL. HIST CALIB FACTORS.)

. keep gpcb_id

. quietly bysort gpcb_id:  gen dup = cond(_N==1,0,_n)

. drop if dup>1
(6,716 observations deleted)

. drop dup

. 
. merge 1:1 gpcb_id using `data', gen(D_analysis)

    Result                      Number of obs
    -----------------------------------------
    Not matched                            26
        from master                         0  (D_analysis==1)
        from using                         26  (D_analysis==2)

    Matched                               292  (D_analysis==3)
    -----------------------------------------

. label define D_analysis 0 "Excluded in analysis" 1 "Included in analysis"

. replace D_analysis = 0 if D_analysis==2
(26 real changes made)

. replace D_analysis = 1 if D_analysis==3
(292 real changes made)

. label values D_analysis D_analysis

. label var D_analysis "=1 if included in emissions analysis"

. 
. order D_analysis, after(D_treatment)

. 
. save "$PHONE_DATA_OUT/apcd_panel_plant.dta", replace
(file 02DataPipeline/phone_survey/apcd_panel_plant.dta not found)
file 02DataPipeline/phone_survey/apcd_panel_plant.dta saved

. 
end of do-file

. 
. * Table 5 and Table F2
. do "$CODE_DIR/create_regression_tables_KW.do"

. /*********************************************************************
> Purpose:        Run regressions with phone survey and baseline survey data.
> *********************************************************************/
. 
. set more off

. clear matrix

. clear all

. pause on

. *ssc install estout
.         
. *------------------------------------------------------------------------
. 
. use "$PHONE_DATA_OUT/PhoneBaseline.dta", clear
(337 STACKS. MASTER T&C BALANCED PANEL. HIST CALIB FACTORS.)

. 
. ************************************************
. * Factors of Production
. ************************************************
. 
.         est clear

.         
.         // Create Total Costs
.         foreach survey in bl el {
  2.                 local plant_varlist plant_cost_labor_`survey' plant_cost_elec_`survey' plant_cost_material_`survey'
  3.                 local bh_varlist bh_cost_capital_`survey' bh_cost_labor_`survey' bh_cost_fuel_`survey' bh_cost_material_`survey'
  4.                 
.                 gen plant_cost_total_`survey' = 0, before(plant_cost_capital_el)
  5.                 gen bh_cost_total_`survey' = 0, before(bh_cost_capital_el)
  6.                 foreach var of varlist `plant_varlist' {
  7.                         replace plant_cost_total_`survey' = plant_cost_total_`survey' + `var'
  8.                 }
  9.                 foreach var of varlist `bh_varlist' {
 10.                         replace bh_cost_total_`survey' = bh_cost_total_`survey' + `var'
 11.                 }
 12.         }
(283 real changes made, 7 to missing)
(276 real changes made, 15 to missing)
(241 real changes made)
(276 real changes made)
(283 real changes made, 7 to missing)
(276 real changes made, 52 to missing)
(209 real changes made)
(283 real changes made, 27 to missing)
(246 real changes made, 12 to missing)
(239 real changes made)
(283 real changes made, 65 to missing)
(218 real changes made, 8 to missing)
(210 real changes made, 16 to missing)
(185 real changes made)

.         
.         // Run regressions: non-BH
.                 
.         * w/ control
.         foreach var in total elec labor material {
  2.                 eststo reg_plant_`var': reg plant_cost_`var'_el treat plant_cost_`var'_bl, robust
  3.                 quietly sum plant_cost_`var'_el if treat==0 & !missing(plant_cost_`var'_el) & !missing(plant_cost_`var'_bl)
  4.                 estadd scalar mean = `r(mean)'
  5.                 estadd local ctrl_mean = string(r(mean), "%15.2f")
  6.         }

Linear regression                               Number of obs     =        224
                                                F(2, 221)         =       3.09
                                                Prob > F          =     0.0473
                                                R-squared         =     0.0320
                                                Root MSE          =     1776.5

-------------------------------------------------------------------------------------
                    |               Robust
plant_cost_total_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
              treat |   258.0052    233.785     1.10   0.271     -202.728    718.7385
plant_cost_total_bl |   1.472425   .7321525     2.01   0.046     .0295312    2.915319
              _cons |    908.048   187.3014     4.85   0.000     538.9225    1277.173
-------------------------------------------------------------------------------------

added scalar:
               e(mean) =  1193.2376

added macro:
          e(ctrl_mean) : "1193.24"

Linear regression                               Number of obs     =        247
                                                F(2, 244)         =      38.21
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6465
                                                Root MSE          =     100.75

------------------------------------------------------------------------------------
                   |               Robust
plant_cost_elec_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
             treat |   25.21299   13.53032     1.86   0.064    -1.438135    51.86412
plant_cost_elec_bl |   .7276843   .1062637     6.85   0.000      .518373    .9369956
             _cons |   41.38426   13.52879     3.06   0.002     14.73614    68.03237
------------------------------------------------------------------------------------

added scalar:
               e(mean) =  162.132

added macro:
          e(ctrl_mean) : "162.13"

Linear regression                               Number of obs     =        249
                                                F(2, 246)         =       3.01
                                                Prob > F          =     0.0513
                                                R-squared         =     0.0201
                                                Root MSE          =     267.38

-------------------------------------------------------------------------------------
                    |               Robust
plant_cost_labor_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
              treat |   19.07534    34.1703     0.56   0.577    -48.22833    86.37902
plant_cost_labor_bl |    2.32725   .9612765     2.42   0.016     .4338676    4.220632
              _cons |   256.5572   34.52046     7.43   0.000     188.5639    324.5506
-------------------------------------------------------------------------------------

added scalar:
               e(mean) =  306.36578

added macro:
          e(ctrl_mean) : "306.37"

Linear regression                               Number of obs     =        283
                                                F(2, 280)         =       2.50
                                                Prob > F          =     0.0836
                                                R-squared         =     0.0066
                                                Root MSE          =     2060.8

----------------------------------------------------------------------------------------
                       |               Robust
plant_cost_material_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
                 treat |   302.4401   250.0619     1.21   0.228    -189.7998    794.6799
plant_cost_material_bl |   4.080315   6.119565     0.67   0.505    -7.965881    16.12651
                 _cons |   743.6212   115.7361     6.43   0.000     515.7979    971.4445
----------------------------------------------------------------------------------------

added scalar:
               e(mean) =  764.16542

added macro:
          e(ctrl_mean) : "764.17"

.         * blank cols
.         foreach var in capital fuel {
  2.                 eststo reg_plant_`var': reg plant_cost_`var'_el, robust
  3.                 estadd local N = "", replace
  4.                 estadd local r2 = "", replace
  5.         }

Linear regression                               Number of obs     =        218
                                                F(0, 217)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =          .
                                                Root MSE          =          0

------------------------------------------------------------------------------
             |               Robust
plan~ital_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _cons |          0  (omitted)
------------------------------------------------------------------------------

added macro:
                  e(N) : ""

added macro:
                 e(r2) : ""

Linear regression                               Number of obs     =        238
                                                F(0, 237)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =          .
                                                Root MSE          =          0

------------------------------------------------------------------------------
             |               Robust
plant_~el_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _cons |          0  (omitted)
------------------------------------------------------------------------------

added macro:
                  e(N) : ""

added macro:
                 e(r2) : ""

.         
.         
.         // Run regressions - BH
.         
.         * w/ control 
.         foreach var in total capital labor fuel material {
  2.                 eststo reg_bh_`var': reg bh_cost_`var'_el treat bh_cost_`var'_bl, robust
  3.                 sum bh_cost_`var'_el if treat==0 & !missing(bh_cost_`var'_el) & !missing(bh_cost_`var'_bl)
  4.                 estadd scalar mean = `r(mean)'
  5.                 estadd local ctrl_mean = string(r(mean), "%15.2f")
  6.         }

Linear regression                               Number of obs     =        185
                                                F(2, 182)         =     101.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9252
                                                Root MSE          =     184.19

----------------------------------------------------------------------------------
                 |               Robust
bh_cost_total_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
           treat |   11.25749    26.3142     0.43   0.669    -40.66264    63.17763
bh_cost_total_bl |   1.437234   .1012173    14.20   0.000     1.237524    1.636945
           _cons |  -23.15728   41.65767    -0.56   0.579    -105.3514     59.0368
----------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bh_cost_t~el |         81    578.4751    514.6586    86.6396   3151.312

added scalar:
               e(mean) =  578.47505

added macro:
          e(ctrl_mean) : "578.48"

Linear regression                               Number of obs     =        218
                                                F(2, 215)         =      21.03
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6272
                                                Root MSE          =     159.93

------------------------------------------------------------------------------------
                   |               Robust
bh_cost_capital_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
             treat |  -7.177682   19.04599    -0.38   0.707    -44.71845    30.36308
bh_cost_capital_bl |   3.075353   .5770426     5.33   0.000     1.937967    4.212738
             _cons |  -12.45443   38.21749    -0.33   0.745    -87.78336    62.87451
------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bh_cost_c~el |         99    190.8797     189.132   3.830717   1383.198

added scalar:
               e(mean) =  190.8797

added macro:
          e(ctrl_mean) : "190.88"

Linear regression                               Number of obs     =        262
                                                F(2, 259)         =       1.16
                                                Prob > F          =     0.3146
                                                R-squared         =     0.0454
                                                Root MSE          =      28.99

----------------------------------------------------------------------------------
                 |               Robust
bh_cost_labor_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
           treat |    1.56052    3.33181     0.47   0.640    -5.000365    8.121406
bh_cost_labor_bl |   .2707606   .1818312     1.49   0.138    -.0872952    .6288164
           _cons |   42.09731   4.436903     9.49   0.000     33.36032    50.83431
----------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bh_cost_l~el |        122    47.86047    24.43318       5.04     152.04

added scalar:
               e(mean) =  47.860472

added macro:
          e(ctrl_mean) : "47.86"

Linear regression                               Number of obs     =        225
                                                F(2, 222)         =    1022.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9791
                                                Root MSE          =     111.51

---------------------------------------------------------------------------------
                |               Robust
bh_cost_fuel_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
          treat |   26.86852   15.34644     1.75   0.081    -3.374813    57.11185
bh_cost_fuel_bl |   1.055474   .0238787    44.20   0.000     1.008416    1.102532
          _cons |  -23.49696   12.71853    -1.85   0.066    -48.56145    1.567534
---------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bh_cost_f~el |        103    299.4961    290.2875      1.176   1719.898

added scalar:
               e(mean) =  299.49608

added macro:
          e(ctrl_mean) : "299.50"

Linear regression                               Number of obs     =        283
                                                F(2, 280)         =       1.65
                                                Prob > F          =     0.1933
                                                R-squared         =     0.1904
                                                Root MSE          =     8.1236

-------------------------------------------------------------------------------------
                    |               Robust
bh_cost_material_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
              treat |  -.1423985    .595615    -0.24   0.811     -1.31485    1.030053
bh_cost_material_bl |   .2951744   .1698066     1.74   0.083    -.0390853     .629434
              _cons |   2.846225   .9515346     2.99   0.003     .9731552    4.719294
-------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bh_cost_m~el |        132    4.332416     5.48069          0     30.625

added scalar:
               e(mean) =  4.332416

added macro:
          e(ctrl_mean) : "4.33"

.         * blank cols
.         foreach var in elec {
  2.                 eststo reg_bh_`var': reg bh_cost_`var'_el, robust
  3.                 estadd local N = "", replace
  4.                 estadd local r2 = "", replace
  5.         }

Linear regression                               Number of obs     =        283
                                                F(0, 282)         =       0.00
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     340.61

------------------------------------------------------------------------------
             |               Robust
bh_cost_e~el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _cons |   166.3022   20.24705     8.21   0.000     126.4476    206.1567
------------------------------------------------------------------------------

added macro:
                  e(N) : ""

added macro:
                 e(r2) : ""

.                                 
. ********************************************************************
. * Abatement Devices (Count + Any + Cost)
. ********************************************************************
. 
. 
.         //      Create total number of APCDs and dummy for any APCDs 
.         foreach survey in bl el {
  2.                 gen apcds_total_cost_`survey' = cyclones_total_cost_`survey' + bagfilters_total_cost_`survey' + scrubbers_total_cost_`survey' + esps_total_cost_`survey', after(esps_total_cost_`survey')
  3.                 gen num_apcds_`survey' = num_cyclones_`survey' + num_scrubbers_`survey' + num_bagfilters_`survey' + num_esps_`survey', after(num_esps_`survey')
  4.                 gen D_apcds_`survey' = (num_apcds_`survey' > 0), after(num_apcds_`survey')
  5.                 replace D_apcds_`survey' = . if num_apcds_`survey'==.
  6.         }       
(7 missing values generated)
(7 missing values generated)
(7 real changes made, 7 to missing)
(0 real changes made)

. 
.         
.         // Run Regressions 
.         foreach var in apcds cyclones bagfilters scrubbers esps {
  2.                 * count
.                 eststo reg_num_`var': reg num_`var'_el treat num_`var'_bl, robust
  3.                 sum num_`var'_el if treat==0
  4.                 estadd scalar mean = `r(mean)'
  5.                 estadd local ctrl_mean = string(r(mean), "%15.2f")
  6.                 
.                 * cost
.                 eststo reg_cost_`var': reg `var'_total_cost_el treat `var'_total_cost_bl, robust
  7.                 sum `var'_total_cost_el if treat==0
  8.                 estadd scalar mean = `r(mean)'
  9.                 estadd local ctrl_mean = string(r(mean), "%15.2f")
 10.         }

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     133.60
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8112
                                                Root MSE          =     .73337

------------------------------------------------------------------------------
             |               Robust
num_apcds_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .1227601   .0892144     1.38   0.170    -.0528756    .2983958
num_apcds_bl |   .9730084   .0596452    16.31   0.000     .8555853    1.090431
       _cons |   .2208294   .2819648     0.78   0.434    -.3342723    .7759311
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
num_apcds_el |        132    4.863636    1.429102          1         10

added scalar:
               e(mean) =  4.8636364

added macro:
          e(ctrl_mean) : "4.86"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     136.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9033
                                                Root MSE          =      29.66

-------------------------------------------------------------------------------------
                    |               Robust
apcds_total_cost_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
              treat |  -3.467003   3.089231    -1.12   0.263    -9.548747     2.61474
apcds_total_cost_bl |   1.054027   .0720491    14.63   0.000     .9121843    1.195869
              _cons |   3.905183   3.643717     1.07   0.285    -3.268172    11.07854
-------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
apcds_tot~el |        132    44.04034    55.93635      5.425      483.7

added scalar:
               e(mean) =  44.040341

added macro:
          e(ctrl_mean) : "44.04"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =      49.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7725
                                                Root MSE          =     .50888

---------------------------------------------------------------------------------
                |               Robust
num_cyclones_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
          treat |   .1347518   .0573845     2.35   0.020     .0217794    .2477241
num_cyclones_bl |   .9773573   .1005699     9.72   0.000     .7793662    1.175348
          _cons |  -.0239454    .198973    -0.12   0.904     -.415662    .3677711
---------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
num_cyclo~el |        132    1.931818    .7225187          0          5

added scalar:
               e(mean) =  1.9318182

added macro:
          e(ctrl_mean) : "1.93"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =      78.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8494
                                                Root MSE          =     2.3102

----------------------------------------------------------------------------------------
                       |               Robust
cyclones_total_cost_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
                 treat |   .6020471   .2658912     2.26   0.024     .0785893    1.125505
cyclones_total_cost_bl |   1.050136   .0870468    12.06   0.000     .8787675    1.221504
                 _cons |  -.7383908   .7058009    -1.05   0.296    -2.127895    .6511136
----------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
cyclones_~el |        132    7.800758    3.410015          0      22.75

added scalar:
               e(mean) =  7.8007576

added macro:
          e(ctrl_mean) : "7.80"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     378.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8002
                                                Root MSE          =     .36195

-----------------------------------------------------------------------------------
                  |               Robust
num_bagfilters_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
            treat |   .0733432    .044012     1.67   0.097    -.0133028    .1599893
num_bagfilters_bl |   .8835982   .0322221    27.42   0.000     .8201628    .9470336
            _cons |   .1577935   .0670637     2.35   0.019     .0257657    .2898213
-----------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
num_bagfi~el |        132    1.530303    .8236893          0          5

added scalar:
               e(mean) =  1.530303

added macro:
          e(ctrl_mean) : "1.53"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     473.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8269
                                                Root MSE          =     2.5814

------------------------------------------------------------------------------------------
                         |               Robust
bagfilters_total_cost_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
                   treat |   .5300479   .3181633     1.67   0.097    -.0963176    1.156413
bagfilters_total_cost_bl |    .895974   .0295504    30.32   0.000     .8377984    .9541495
                   _cons |   .8015902   .3935409     2.04   0.043     .0268296    1.576351
------------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
bagfilter~el |        132    9.846402    6.374842          0         42

added scalar:
               e(mean) =  9.8464017

added macro:
          e(ctrl_mean) : "9.85"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     485.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7743
                                                Root MSE          =     .44129

----------------------------------------------------------------------------------
                 |               Robust
num_scrubbers_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
           treat |  -.0482124   .0547648    -0.88   0.379    -.1560275    .0596027
num_scrubbers_bl |    .879468   .0296051    29.71   0.000     .8211846    .9377513
           _cons |   .2479698   .0694608     3.57   0.000     .1112229    .3847168
----------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
num_scrub~el |        132    1.227273    .9376836          0          3

added scalar:
               e(mean) =  1.2272727

added macro:
          e(ctrl_mean) : "1.23"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     742.19
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8437
                                                Root MSE          =     3.2868

-----------------------------------------------------------------------------------------
                        |               Robust
scrubbers_total_cost_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
                  treat |  -.2224627   .4073017    -0.55   0.585    -1.024314    .5793889
scrubbers_total_cost_bl |   .9234227   .0246629    37.44   0.000      .874869    .9719763
                  _cons |   1.434231   .4686461     3.06   0.002     .5116109     2.35685
-----------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
scrubbers~el |        132    9.688636    8.211681          0       31.5

added scalar:
               e(mean) =  9.6886364

added macro:
          e(ctrl_mean) : "9.69"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     107.99
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7944
                                                Root MSE          =     .31057

------------------------------------------------------------------------------
             |               Robust
 num_esps_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |  -.0542385   .0345958    -1.57   0.118    -.1223469      .01387
 num_esps_bl |   .9867707   .0765361    12.89   0.000     .8360947    1.137447
       _cons |   .0706555    .032773     2.16   0.032     .0061355    .1351755
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 num_esps_el |        132    .1742424     .531399          0          3

added scalar:
               e(mean) =  .17424242

added macro:
          e(ctrl_mean) : "0.17"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     126.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.8866
                                                Root MSE          =     31.814

------------------------------------------------------------------------------------
                   |               Robust
esps_total_cost_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
             treat |  -4.280534   3.344076    -1.28   0.202    -10.86399    2.302921
esps_total_cost_bl |   1.040202   .0763436    13.63   0.000     .8899051    1.190499
             _cons |   5.339282   2.958392     1.80   0.072    -.4848791    11.16344
------------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
esps_tota~el |        132    16.70455    58.72659          0      472.5

added scalar:
               e(mean) =  16.704545

added macro:
          e(ctrl_mean) : "16.70"

.         
.         foreach var in apcds cyc bf scr esp {
  2.                 * any
.                 eststo reg_D_`var': reg D_`var'_el treat D_`var'_bl, robust
  3.                 sum D_`var'_el if treat==0
  4.                 estadd scalar mean = `r(mean)'
  5.                 estadd local ctrl_mean = string(r(mean), "%15.2f")
  6.         }
note: D_apcds_bl omitted because of collinearity.

Linear regression                               Number of obs     =        276
                                                F(0, 274)         =          .
                                                Prob > F          =          .
                                                R-squared         =          .
                                                Root MSE          =          0

------------------------------------------------------------------------------
             |               Robust
  D_apcds_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |          0  (omitted)
  D_apcds_bl |          0  (omitted)
       _cons |          1          .        .       .            .           .
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  D_apcds_el |        132           1           0          1          1

added scalar:
               e(mean) =  1

added macro:
          e(ctrl_mean) : "1.00"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =   25191.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6635
                                                Root MSE          =     .10359

------------------------------------------------------------------------------
             |               Robust
    D_cyc_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0233463   .0133925     1.74   0.082    -.0030194     .049712
    D_cyc_bl |   .9842196   .0101277    97.18   0.000     .9642813    1.004158
       _cons |  -.0077821   .0063199    -1.23   0.219     -.020224    .0046598
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    D_cyc_el |        132    .9469697    .2249476          0          1

added scalar:
               e(mean) =  .9469697

added macro:
          e(ctrl_mean) : "0.95"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     152.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6847
                                                Root MSE          =     .20475

------------------------------------------------------------------------------
             |               Robust
     D_bf_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0649984   .0230683     2.82   0.005      .019584    .1104128
     D_bf_bl |   .8251075   .0544121    15.16   0.000     .7179868    .9322282
       _cons |    .116251   .0528331     2.20   0.029     .0122389    .2202632
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     D_bf_el |        132    .8484848    .3599162          0          1

added scalar:
               e(mean) =  .84848485

added macro:
          e(ctrl_mean) : "0.85"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =     241.86
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7112
                                                Root MSE          =     .25401

------------------------------------------------------------------------------
             |               Robust
    D_scr_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |  -.0150875    .030955    -0.49   0.626    -.0760282    .0458533
    D_scr_bl |     .82325   .0376516    21.86   0.000     .7491256    .8973745
       _cons |   .1562827   .0411901     3.79   0.000      .075192    .2373733
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    D_scr_el |        132    .6742424    .4704426          0          1

added scalar:
               e(mean) =  .67424242

added macro:
          e(ctrl_mean) : "0.67"

Linear regression                               Number of obs     =        276
                                                F(2, 273)         =    4682.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7548
                                                Root MSE          =     .16768

------------------------------------------------------------------------------
             |               Robust
    D_esp_el | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |  -.0311426   .0207033    -1.50   0.134    -.0719009    .0096158
    D_esp_bl |   .9716886   .0105434    92.16   0.000     .9509318    .9924453
       _cons |   .0483316   .0190447     2.54   0.012     .0108385    .0858248
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    D_esp_el |        132    .1212121     .327617          0          1

added scalar:
               e(mean) =  .12121212

added macro:
          e(ctrl_mean) : "0.12"

.         
.         
.         //      Create Tables
.         
. local titles "&\multicolumn{1}{c}{All} &\multicolumn{1}{c}{Cyclone} &\multicolumn{1}{c}{Bag} &\multicolumn{1}{c}{Scrubber} &\multicolumn{1}{c}{ESP} &\multicolumn{1}{c}{Total} &\multicolumn{1}{c}{Capital} &\multicolumn{1}{c}{Labor} &\multicolumn{1}{c}{El
> ectricity} &\multicolumn{1}{c}{Fuel}  &\multicolumn{1}{c}{Materials} \\"

. local numbers "&\multicolumn{1}{c}{(1)} &\multicolumn{1}{c}{(2)} &\multicolumn{1}{c}{(3)} &\multicolumn{1}{c}{(4)} &\multicolumn{1}{c}{(5)} &\multicolumn{1}{c}{(6)} &\multicolumn{1}{c}{(7)} &\multicolumn{1}{c}{(8)} &\multicolumn{1}{c}{(9)} &\multicolumn
> {1}{c}{(10)}  &\multicolumn{1}{c}{(11)} \\"                                                           

. 
. 
. # delimit ;
delimiter now ;
. esttab reg_cost_apcds reg_cost_cyclones reg_cost_bagfilters reg_cost_scrubbers reg_cost_esps reg_bh_total reg_bh_capital reg_bh_labor reg_plant_elec reg_bh_fuel reg_bh_material
>         using "$PHONE_TABS/Table_5.tex", replace 
>         se ar2 booktabs varwidth(32)
>         keep(treat) coeflabel(treat "ETS Treatment (=1)")
>         mgroups("\shortstack{Abatement capital costs (\\$1000s)}" "\shortstack{Boiler house input costs (\\$1000s)}", 
>         pattern(1 0 0 0 0 1 0 0 0 0 0)
>         prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span}))
>         prehead("{" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\begin{tabular}{l*{11}{c}}" "\toprule")
>         posthead("`titles'" "`numbers'" "\midrule \addlinespace")
>         prefoot("\addlinespace \addlinespace")
>         star(* 0.10 ** 0.05 *** 0.01) nonotes nonumbers nomtitles
>         stats(r2 ctrl_mean N, label("R\textsuperscript{2}" "Control mean" "Plants") fmt(%9.2f %9.2f %1s))
>         ;
(file 03Output/tables//Table_5.tex not found)
(output written to 03Output/tables//Table_5.tex)

. #delimit cr
delimiter now cr
. 
. 
. 
. local super_title "&  & \multicolumn{4}{c}{Components} \\  \cmidrule{3-6}"

. local titles "& \multirow{-2}{*}{\shortstack{All \\ APCDs}}  & Cyclone & Bag & Scrubber & ESP \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\"

. 
. 
. # delimit ;
delimiter now ;
. esttab reg_D_apcds reg_D_cyc reg_D_bf reg_D_scr reg_D_esp 
>         using "$PHONE_TABS/Table_F2.tex", replace 
>         se r2 booktabs varwidth(32)             
>         drop(_cons *_bl) coeflabel(treat "ETS Treatment (=1)")
>         nomtitles nonumbers fragment
>         star(* 0.10 ** 0.05 *** 0.01)
>         stats(r2 ctrl_mean N, label("R\textsuperscript{2}" "Control mean" "Plants") fmt(%9.2f %9.2f %1s))
>         prefoot(\addlinespace\addlinespace)
>         nonotes
>         prehead("{" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\begin{tabular}{l*{5}{c}}" "\toprule")
>         posthead("`super_title'" "`titles'" "`numbers'" "\midrule \addlinespace")
>     postfoot("\bottomrule" "\end{tabular}""}")
>         ;
(file 03Output/tables//Table_F2.tex not found)
(output written to 03Output/tables//Table_F2.tex)

.         #delimit cr
delimiter now cr
.         
.         
. est clear

. 
end of do-file

. 
. * Table F1
. do "$CODE_DIR/create_cost_tables.do"

. /*********************************************************************
> Authors: Vineet Gupta
> Project: ETS
> Purpose: Create costs tables
>                         
> Date created: 14 January 2022
> Version:          STATA 16 MP
> 
> Last edited: 14 June 2022
> Edited by:       Vineet, Kaixin
> *********************************************************************/
. 
. clear all

. clear matrix

. set more off

. set linesize 255

. pause on

. 
. use "$PHONE_DATA_OUT/apcd_panel_plant.dta", clear
(337 STACKS. MASTER T&C BALANCED PANEL. HIST CALIB FACTORS.)

. 
. **********************
. 
. * make sure key data is available + plant is used in analysis
. keep if D_treatment==1 & D_analysis==1
(162 observations deleted)

. keep if !missing(emissions_conc_etsbl) & !missing(emissions_mass_etsbl)
(5 observations deleted)

. 
. * make sure a boiler is present
. merge 1:1 gpcb_id using "$BASELINE_DATA_IN/BaselineCovariates_318i.dta", keepusing(plant_boi_cap industry_id vol_flow_etsbl) keep(master match) nogen

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               151  
    -----------------------------------------

. keep if plant_boi_cap!=0
(3 observations deleted)

. 
. 
. * select several plants by boiler capacity roughly corresponding to 5th, 35th, 65th, and 95th percentiles
. _pctile plant_boi_cap, percentiles(5, 35, 65, 95)

. dis "5th: `r(r1)', 35th: `r(r2)', 65th: `r(r3)', and 95th: `r(r4)'"
5th: 3, 35th: 6, 65th: 8, and 95th: 16

. keep if inlist(gpcb_id, 101235423, 104860678, 105331799, 112914615)
(144 observations deleted)

. sort plant_boi_cap

. list plant_boi_cap

     +----------+
     | plant_~p |
     |----------|
  1. |        3 |
  2. |        6 |
  3. |        8 |
  4. |       15 |
     +----------+

. 
. * Previously, the code to select these plants was below (lines 43 to 53), but due to the anonymization 
. * process, the sort order has changed and the same plants are not selected as in the paper.
. * For the purpose of replicating the paper exhibits, the plants are selected manually above.
. 
. * sort plant_boi_cap, stable
. * gen size_n = _n
. 
. * quietly: count 
. * local N_plants `r(N)'
. * local grp1 = floor(`N_plants'*5/100)
. * local grp2 = floor(`N_plants'*35/100)
. * local grp3 = floor(`N_plants'*65/100)
. *local grp4 = floor(`N_plants'*95/100)
. 
. *keep if inlist(size_n, `grp1', `grp2', `grp3', `grp4')
. 
. 
. * replace baseline mass with inlet mass
. gen emissions_inlet_hr = vol_flow_etsbl/1000000 * 2000 // assume inlet conc is 2000 mg/Nm3

. 
. * select pertinent data; reshape for easier manipulation
. keep plant_boi_cap heatoutput apcd_unitcost_* emissions_inlet_hr

. reshape long apcd_unitcost_install_ apcd_unitcost_ope_ apcd_unitcost_maint_, i(heatoutput plant_boi_cap) j(apcd) string
(j = bagfilter cyclone esp scrubber)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                4   ->   16          
Number of variables                  15   ->   7           
j variable (4 values)                     ->   apcd
xij variables:
apcd_unitcost_install_bagfilter apcd_unitcost_install_cyclone ... apcd_unitcost_install_scrubber->apcd_unitcost_install_
apcd_unitcost_ope_bagfilter apcd_unitcost_ope_cyclone ... apcd_unitcost_ope_scrubber->apcd_unitcost_ope_
apcd_unitcost_maint_bagfilter apcd_unitcost_maint_cyclone ... apcd_unitcost_maint_scrubber->apcd_unitcost_maint_
-----------------------------------------------------------------------------

. 
. // ASSUME CYCLONE PRESENT -------------------------------------------
. preserve 

. * construct key variables 
. 
.         * create place-holder assumed unabated pollution
.         gen emissions_inlet_month = emissions_inlet_hr * 16 * 25 * 0.2 // * hours/day * days/month * one cyclone present

.         drop emissions_inlet_hr

.         label var emissions_inlet_month "Assumed uncontrolled pollution (kg/month)"

. 
.         * create monthly amortized flow costs
.         rename apcd_unitcost_*_ cost_*

.         replace cost_install = cost_install * 0.2086 * 1/12 
(16 real changes made)

.         gen cost_var = cost_maint/12 + cost_ope/12 

.         drop cost_maint cost_ope

.         label var cost_install "Capital costs (Rs, monthly amortized)"

.         label var cost_var "Operating and maintenance costs (Rs, monthly)"

.         
.         * create a max efficiency column
.         gen eff = . 
(16 missing values generated)

.         label var eff "Emission reduction (%)"

.         replace eff = 80 if apcd=="cyclone"
(4 real changes made)

.         replace eff = 99 if apcd=="bagfilter"
(4 real changes made)

.         replace eff = 94 if apcd=="scrubber"
(4 real changes made)

.         replace eff = 99.7 if apcd=="esp"
(4 real changes made)

. 
.         * label apcd column
.         label var apcd "APCD"

. 
.         * create emissions abatement 
.         gen emissions_abate_month = emissions_inlet_month * (eff/100)

.         label var emissions_abate_month "Emission abatement (kg/month)"

.         
.         * create monthly costs of abatement
.         gen cost_abate_avg = (cost_install + cost_var)/emissions_abate_month  

.         gen cost_abate_var = cost_var/emissions_abate_month

.         label var cost_abate_avg "Average cost (Rs/kg)"

.         label var cost_abate_var "Variable cost (Rs/kg)"

.         
.         * order of table 
.         sort plant_boi_cap heatoutput

.         order heatoutput plant_boi_cap apcd cost_install cost_var eff emissions_inlet_month emissions_abate_month cost_abate_avg cost_abate_var

.         format cost_install cost_var emissions_abate_month emissions_inlet_month cost_abate_avg cost_abate_var %9.2f

. 
. * Create output table
.         
.         * loop over each boiler size panel
.         clear matrix

.         foreach size in 3 6 8 15 {
  2. 
.                 * save all values for each apcd and cost var into matrices 
.                 tempname mat_cyclone mat_bagfilter mat_scrubber mat_esp
  3.                 ds cost_install cost_var eff emissions_inlet_month emissions_abate_month cost_abate_avg cost_abate_var 
  4.                 foreach var of varlist `r(varlist)' {
  5.                         foreach apcd in bagfilter cyclone scrubber esp {
  6.                                 quietly: summarize `var' if apcd=="`apcd'" & plant_boi_cap==`size'
  7.                                 local est_`apcd' `r(mean)'
  8.                                 mat mat_`apcd' = nullmat(mat_`apcd'), `est_`apcd''
  9.                         }
 10.                 }
 11.                 
.                 * assemble overall panel matrix 
.                 mat panel_`size' = [mat_cyclone', mat_bagfilter', mat_scrubber', mat_esp']
 12.                 mat colnames panel_`size' = "_" "_" "_" "_" // "Cyclone" "Bag Filter" "Scrubber" "ESP"
 13.                 mat rownames panel_`size' = ///
>                         "Capital costs (Rs/month, amort.)" ///
>                         "Variable costs (Rs/month)" ///
>                         "Emission reduction (\%)" ///
>                         "Assumed pollution (kg/month)" ///
>                         "Emission abatement (kg/month)" ///
>                         "Average abatement cost (Rs/kg)" ///
>                         "Variable abatement cost (Rs/kg)" 
 14.                 matrix drop mat_cyclone mat_bagfilter mat_scrubber mat_esp
 15.         }
cost_install  cost_var      eff           emis~t_month  emis~e_month  cost_abate~g  cost_abate~r
cost_install  cost_var      eff           emis~t_month  emis~e_month  cost_abate~g  cost_abate~r
cost_install  cost_var      eff           emis~t_month  emis~e_month  cost_abate~g  cost_abate~r
cost_install  cost_var      eff           emis~t_month  emis~e_month  cost_abate~g  cost_abate~r

. 
.                 * create tables 
.                 local titles "&Cyclone & Bag Filter & Scrubber & ESP \\"

.                 local numbers "& (1) & (2) & (3) & (4) \\"

.                 local file "$PHONE_TABS/Table_F1.tex"

.                 # delimit ;
delimiter now ;
.                                 local table_options 
>                                 "varwidth(32) nomtitles booktabs 
>                                 mlabels(,none) collabels(,none)
>                                 prefoot(\addlinespace) nonotes";

.                                 esttab m(panel_3, fmt(%9.2f)) using "`file'", replace
>                         `table_options'
>                         prehead("{" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\begin{tabular}{l*{5}{c}}" "\toprule")
>                         posthead("`titles'" "`numbers'" "\midrule \addlinespace" "&\multicolumn{4}{c}{\textit{Total Boiler Capacity = 3 TPH}}\\ \addlinespace") 
>                         postfoot("") ;
(file 03Output/tables//Table_F1.tex not found)
(output written to 03Output/tables//Table_F1.tex)

.                                         esttab m(panel_6, fmt(%9.2f)) using "`file'", append
>                         `table_options'
>                         prehead("&\multicolumn{4}{c}{\textit{Total Boiler Capacity = 6 TPH}}\\ \addlinespace") 
>                         posthead("")
>                         postfoot("") ;
(output written to 03Output/tables//Table_F1.tex)

.                                         esttab m(panel_8, fmt(%9.2f)) using "`file'", append
>                         `table_options'
>                         prehead("&\multicolumn{4}{c}{\textit{Total Boiler Capacity = 8 TPH}}\\ \addlinespace") 
>                         posthead("") postfoot("") ;
(output written to 03Output/tables//Table_F1.tex)

.                                         esttab m(panel_15, fmt(%9.2f)) using "`file'", append ///
>                         `table_options'
>                         prehead("&\multicolumn{4}{c}{\textit{Total Boiler Capacity = 15 TPH}}\\ \addlinespace")
>                         posthead("") postfoot("\bottomrule" "\end{tabular}""}") ;
(output written to 03Output/tables//Table_F1.tex)

.                                         # delimit cr
delimiter now cr
. 
. restore 

. 
end of do-file

. 
end of do-file
